Test server, communication terminal, test system, and test method

ABSTRACT

A test server includes: a communication unit that communicates with a plurality of communication terminals via a network, the plurality of communication terminals each being connectable to a test device capable of executing a test on the presence or absence of a disease and each being capable of inputting a diagnosis on the presence or absence of the disease, the diagnosis being related to the test and made by a doctor; and a control unit that acquires at least one of a result of the test and the diagnosis as a test information item from each of the plurality of communication terminals via the communication unit, causes a storage unit to store the plurality of acquired test information items therein, performs statistical processing on the plurality of stored test information items, and causes the communication unit to return a result of the statistical processing according to a demand given from each of the communication terminals before the doctor makes a diagnosis.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a divisional application of U.S. patent applicationSer. No. 15/103,958, filed on Jun. 13, 2016, is a U.S. National Phase ofInternational Patent Application No. PCT/JP2014/005778 filed on Nov. 18,2014, which claims priority benefit of Japanese Patent Application No.JP 2013-265133 filed in the Japan Patent Office on Dec. 24, 2013. Eachof the above-referenced applications is hereby incorporated herein byreference in its entirety.

TECHNICAL FIELD

The present technology relates to a test system using statisticalinformation, to a communication terminal and a test server thatconfigure the test system, and to a test method used in the test system.

BACKGROUND ART

Tests performed in medical care recently have been increasinglyimportant in carrying out treatment of patients. Many test devices, testkits, and test methods are developed for clinical tests.

A test system can also be established as a network compatible clientserver system.

For example, in Patent Document 1, an intelligence module 105 configuredby a computer, for example, receives patient test results from a dataacquisition module such as a test system 150 through a direct connectionor over a network 140. The intelligence module executes a diseaseclassification process for analyzing patient test results to determinewhether a patient sample is associated with an inflammatory boweldisease or a clinical subtype thereof. The determination made by theprocess is then provided to a client system 130.

-   Patent Document 1: Japanese Patent Application Laid-open No.    2012-508383

SUMMARY OF INVENTION Problem to be Solved by the Invention

In the above-mentioned test system, however, an analysis is justperformed based on the patient test results collected from a testterminal (test system) to provide a diagnosis thereof to a client. Inother words, the above-mentioned test system is not a test system thatpreliminarily presents, before a doctor performs a clinical test, apositive predictive value and a negative predictive value (that will bedescribed later) of the test to the doctor, to assist the doctor todetermine whether to perform or stop the test.

Further, there have been no test systems that acquire test results ordiagnoses from other test terminals, which are dispersed in manycountries and regions, via a network, and calculates a prevalence rate(that will be described later), a positive predictive value, and anegative predictive value for each region that are changed with time, toprovide them to a doctor.

Moreover, in order to improve the degree of accuracy of the prevalencerate, it is important to obtain the extremely-huge total number of testresults, but increasing the total number has been not focused on.

Furthermore, there have been no test systems that cope with spread of aninfectious disease, such as pandemic.

In addition, in the above-mentioned test system, there have been variousproblems. For example, it is impossible to enhance the degree ofaccuracy of information to be offered or provide higher-value-addedinformation, based on a larger volume of information.

In view of the circumstances as described above, it is an object of thepresent technology to provide a test server, a communication terminal, atest system, and a test method that improve a clinical test or treatmentin various aspects such as quality and cost.

Means for Solving the Problem

In order to achieve the object described above, according to anembodiment of the present technology, there is provided a test serverincluding: a communication unit that communicates with a plurality ofcommunication terminals via a network, the plurality of communicationterminals each being connectable to a test device capable of executing atest on the presence or absence of a disease and each being capable ofinputting a diagnosis on the presence or absence of the disease, thediagnosis being related to the test and made by a doctor; and a controlunit that acquires at least one of a result of the test and thediagnosis as a test information item from each of the plurality ofcommunication terminals via the communication unit, causes a storageunit to store the plurality of acquired test information items therein,performs statistical processing on the plurality of stored testinformation items, and causes the communication unit to return a resultof the statistical processing according to a demand given from each ofthe communication terminals before the doctor makes a diagnosis. Itshould be noted that the test device used here includes test agents inaddition to an original test device.

In order to achieve the object described above, in the test serveraccording to the embodiment of the present technology, the control unitmay cause the communication unit to return at least one of a prevalencerate, a positive predictive value, and a negative predictive value thatare calculated as the result of the statistical processing, based on thenumber of test information items in which the result of the test and thediagnosis are positive, the number of test information items in whichthe result of the test is negative and the diagnosis is positive, thenumber of test information items in which the result of the test ispositive and the diagnosis is negative, and the number of testinformation items in which the result of the test and the diagnosis arenegative, in the plurality of stored test information items.

In order to achieve the object described above, in the test serveraccording to the embodiment of the present technology, the control unitmay cause the communication unit to return the positive predictive valueand the negative predictive value, in addition to the prevalence rate,the positive predictive value and the negative predictive value beingcalculated based on the prevalence rate, a sensitivity of the testdevice, and a specificity of the test device.

In order to achieve the object described above, in the test serveraccording to the embodiment of the present technology, the control unitmay acquire, from each of the communication terminals, an elapsed timefrom the development of a disease of a patient who is to be subjected tothe test, acquire a sensitivity and a specificity that correspond to theelapsed time from the development of the disease, and calculate thepositive predictive value and the negative predictive value based on theacquired sensitivity and specificity.

In order to achieve the object described above, in the test serveraccording to the embodiment of the present technology, the control unitmay cause the test device to execute various types of tests for testingthe disease, the test device being connected to each of thecommunication terminals, acquire results of the executed various typesof tests from the test device, and determine a result of the testindicating the presence or absence of the disease, based on the acquiredresults of the various types of tests.

In order to achieve the object described above, in the test serveraccording to the embodiment of the present technology, the test devicemay be capable of executing various types of tests, and the control unitmay calculate, after causing the test device to execute one of thevarious types of tests, posttest odds in the one test based on at leastone of a positive likelihood ratio and a negative likelihood ratio onthe one test, transmit the posttest odds to each of the communicationterminals, and acquire information on whether a subsequent test isperformed or not from each of the communication terminals.

In order to achieve the object described above, in the test serveraccording to the embodiment of the present technology, the testinformation items acquired from the communication terminals may eachinclude patient attribute information indicating an attribute of apatient who is subjected to the test, and the control unit may perform,when receiving a demand to narrow down statistical information from eachof the communication terminals, the statistical processing by performingnarrowing-down for test information items each having the attribute ofthe patient attribute information, the demand specifying any patientattribute information.

In order to achieve the object described above, in the test serveraccording to the embodiment of the present technology, the testinformation items acquired from the communication terminals may eachinclude terminal attribute information indicating an attribute of eachof the communication terminals that performs the test, and the controlunit may perform, when receiving a demand to narrow down statisticalinformation from each of the communication terminals, the statisticalprocessing by performing narrowing-down for test information items eachhaving the attribute of the terminal attribute information, the demandspecifying any terminal attribute information.

In order to achieve the object described above, in the test serveraccording to the embodiment of the present technology, the control unitmay perform weighting on the result of the statistical processing, theweighting being based on the terminal attribute information, the resultof the statistical processing being calculated based on the testinformation items obtained by narrowing-down.

In order to achieve the object described above, in the test serveraccording to the embodiment of the present technology, the control unitmay be capable of using a positive rate instead of the prevalence rate.

In order to achieve the object described above, in the test serveraccording to the embodiment of the present technology, the testinformation item may include information for identifying a method ofperforming the test, and the control unit may be capable of using thepositive rate instead of the prevalence rate in each of the methods ofperforming the test for an identical disease, the positive rate beingthe result of the statistical processing performed on a plurality oftest information items acquired by one of the methods, the methodsatisfying preliminarily demanded predetermined values of a sensitivityand a specificity, out of sensitivities and specificities preliminarilyprovided to the respective methods, the prevalence rate being the resultof the statistical processing performed on each of a plurality of testinformation items acquired by another one of the methods.

In order to achieve the object described above, in the test serveraccording to the embodiment of the present technology, the control unitmay evaluate effectiveness of the test based on the positive predictivevalue, transmit an evaluation result to each of the communicationterminals, and cause each of the communication terminals to present amessage of recommendation or non-recommendation for the test.

In order to achieve the object described above, in the test serveraccording to the embodiment of the present technology, the testinformation items acquired from the communication terminals may eachinclude information of a region in which each of the communicationterminals is located, as terminal attribute information indicating anattribute of each of the communication terminals that performs the test,and the control unit may estimate the prevalence rate in a first regionin which the test is not implemented, based on prevalence rates obtainedin one or more second regions that are different from the first region,and based on a factor having an influence on infection between each ofthe second regions and the first region.

In order to achieve the object described above, in the test serveraccording to the embodiment of the present technology, the control unitmay periodically perform the statistical processing and create historyinformation of the prevalence rate, and predict a future prevalence ratebased on the history information.

In order to achieve the object described above, in the test serveraccording to the embodiment of the present technology, the control unitmay return a result of the statistical processing acquired from outside,instead of performing the statistical processing on the plurality ofstored test information items.

In order to achieve the object described above, in the test serveraccording to the embodiment of the present technology, the control unitmay transmit a list of medicines to each of the communication terminals,the medicines being based on at least one of the result of the test, thediagnosis, and the result of the statistical processing, and cause eachof the communication terminals to present the list as medicinesrecommended for medication, or the control unit may cause each of thecommunication terminals to present a list of methods for the testcapable of being performed in the test device, a recommendation markindicating a method for a test recommended in the list, and a userinterface for starting the test.

In order to achieve the object described above, according to anembodiment of the present technology, there is provided a communicationterminal including: a communication unit that communicates with a testserver via a network, the test server collecting a plurality of sets ofat least one of a result of a test on the presence or absence of adisease and a diagnosis on the presence or absence of the disease astest information items, and providing a result of statistical processingperformed on the plurality of collected test information items, thediagnosis being related to the test and made by a doctor; an input unitthat receives an input from a user or the doctor; and a control unitthat causes the communication unit to transmit a demand of the result ofthe statistical processing to the test server, causes a test device toexecute the test, presents the result of the statistical processing anda result of the executed test to the user, the result of the statisticalprocessing being received via the communication unit from the testserver, causes the user to input the diagnosis on the executed test,using the input unit, and causes the communication unit to transmit atleast one of the result of the executed test and the input diagnosis asthe test information item to the test server.

In order to achieve the object described above, according to anembodiment of the present technology, there is provided a test systemincluding: a test server; and a plurality of communication terminals,the test server including a first communication unit that communicateswith the plurality of communication terminals via a network, and a firstcontrol unit that acquires at least one of a result of a test on thepresence or absence of a disease and a diagnosis on the presence orabsence of the disease as a test information item from each of theplurality of communication terminals via the communication unit, thediagnosis being related to the test and made by a doctor, causes astorage unit to store the plurality of acquired test information itemstherein, performs statistical processing on the plurality of stored testinformation items, and causes the communication unit to return a resultof the statistical processing according to a demand given from each ofthe communication terminals before the doctor makes a diagnosis, theplurality of communication terminals each including a secondcommunication unit that communicates with the test server via thenetwork, an input unit that receives an input from a user or the doctor,and a second control unit that causes the communication unit to transmitthe demand of the result of the statistical processing to the testserver, causes a test device to execute the test, presents the result ofthe statistical processing and a result of the executed test to theuser, the result of the statistical processing being received via thecommunication unit from the test server, causes the user to input thediagnosis on the executed test, using the input unit, and causes thecommunication unit to transmit at least one of the result of theexecuted test and the input diagnosis as the test information item tothe test server.

In order to achieve the object described above, according to anembodiment of the present technology, there is provided a test methodincluding: by a control unit, acquiring, from a plurality ofcommunication terminals each being connectable to a test device capableof executing a test on the presence or absence of a disease and eachbeing capable of inputting a diagnosis on the presence or absence of thedisease, at least one of a result of the test and the diagnosis as atest information item via the communication unit, the diagnosis beingrelated to the test and made by a doctor; causing a storage unit tostore the plurality of acquired test information items therein;performing statistical processing on the plurality of stored testinformation items; and causing the communication unit to return a resultof the statistical processing according to a demand given from each ofthe communication terminals before the doctor makes a diagnosis.

In order to achieve the object described above, according to anembodiment of the present technology, there is provided a test methodincluding: by a control unit, causing a communication unit to transmit ademand of a result of statistical processing to a test server, thecommunication unit communicating with the test server via a network, thetest server collecting a plurality of sets of at least one of a resultof a test on the presence or absence of a disease and a diagnosis on thepresence or absence of the disease as test information items, andproviding the result of the statistical processing performed on theplurality of collected test information items, the diagnosis beingrelated to the test and made by a doctor; causing the communication unitto transmit the demand of the result of the statistical processing tothe test server; causing a test device to execute the test; presentingthe result of the statistical processing and a result of the executedtest to a user or the doctor, the result of the statistical processingbeing received via the communication unit from the test server; causingthe user to input the diagnosis on the executed test, using an inputunit that receives an input from the user; and causing the communicationunit to transmit at least one of the result of the executed test and theinput diagnosis as the test information item to the test server.

Effect of the Invention

As described above, according to the present technology, it is possibleto improve a clinical test or treatment in various aspects such asquality and cost. It should be noted that the effects described hereinare not necessarily limited, and any of the effects described herein maybe produced.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 is a diagram showing a state where a clinical test of a certaindisease is performed by a certain test method.

FIG. 2 is a graph showing a relationship between a positive predictivevalue and a negative predictive value, and a prevalence rate.

FIG. 3 is a diagram showing a configuration in which a test system 10that adopts the present technology connects test terminals 20 with atest server 40 via a network.

FIG. 4 is a block diagram of a case where the test server 40 isconfigured by a general computer.

FIG. 5 is a diagram showing an example of fields (items) in each recordthat configures a database 47 a.

FIG. 6 is a block diagram of a case where the test terminal 20 isconfigured by a test device and a general computer.

FIG. 7 is a flowchart for describing the overall processing flow in thetest system 10.

FIG. 8 is a flowchart for describing the details of processing to countand calculate a prevalence rate.

FIG. 9 is a flowchart for describing the details of the implementationof a test.

FIG. 10 is a flowchart for describing processing using a sensitivity anda specificity that are based on an elapsed time from the development ofa disease, in processing to implement a test.

FIG. 11 is a flowchart for describing processing in which various testsare performed and results of the tests are comprehensively used in theprocessing to implement a test.

FIG. 12 is a flowchart for describing processing in which a plurality oftests are executed one by one and each time one test result is obtained,whether the test is continued or not is determined, in the processing toimplement a test.

FIG. 13 is a flowchart for describing processing to count and calculatea prevalence rate after narrowing down count target data based on anadministrative district and a physical distance, in processing to countand calculate a prevalence rate.

FIG. 14 is a flowchart for describing the processing to count andcalculate a prevalence rate after narrowing down count target data basedon gender and an age category of patients, in the processing to countand calculate a prevalence rate.

FIG. 15 is a flowchart of processing to calculate, in the case where thenumber of registered patients is small in the database 47 a andnarrowing-down according to a genetic polymorphism is meaningless, aprevalence rate of that genetic polymorphism by correcting the overallprevalence rate using a predetermined sensitivity.

FIG. 16 is a block diagram showing a configuration example of a testserver 40 a that can correct the prevalence rate using sensitivityinformation.

FIG. 17 is a flowchart of processing to perform weighting correction onthe prevalence rate (diagnosis prevalence rate) calculated by count ofthe database 47 a in a certain administrative district, based on animmunization penetration rate in that administrative district, topredict a true prevalence rate.

FIG. 18 is a flowchart for describing processing using an approximateindex to be a substitute for the prevalence rate, instead of theprevalence rate.

FIG. 19 is a graph showing a relationship between a prevalence rate anda positive rate when the sensitivity and the specificity are changed.

FIG. 20 is a graph showing a relationship between a prevalence rate or apositive rate as a substitute for the prevalence rate, and the positivepredictive value and the negative predictive value.

FIG. 21 is a flowchart for describing processing to count and calculatea prevalence rate in the case where the configuration of a modifiedexample is adopted.

FIG. 22 is a flowchart of processing to recommend implementation oftests or implementation of no tests depending on the level of acalculated positive predictive value.

FIG. 23 is a diagram showing prevalence rates of a plurality of regionsfor which test results are already accumulated, and a state where aprevalence rate of a region where a test is not yet performed ispredicted in accordance with distances from the plurality of regions.

FIG. 24 is a flowchart for describing a processing flow to provide afuture predicted prevalence rate as well, in addition to a currentprevalence rate.

FIG. 25 is a flowchart showing processing on a predeterminedcertain-time-period basis and processing in each implementation of test.

FIG. 26 is a diagram showing a configuration for uploading a diagnosisetc. using the LIS.

FIG. 27 is a diagram showing a specific example in which a list of testmethods feasible by the test device 28 of the test terminal 20 ispresented on the test terminal 20 in addition to a name of a disease, aprevalence rate, a positive predictive value, and a negative predictivevalue, and a recommended test method is further displayed thereon.

MODE(S) FOR CARRYING OUT THE INVENTION

Hereinafter, embodiments of the present technology will be describedwith reference to the drawings.

[Regarding Background]

In test devices, test agents, and test kits (hereinafter, correctivelyreferred to as test device) used in clinical practice, the degree ofaccuracy (=sensitivity) and the degree of accuracy (=specificity) aredefined. With the degree of accuracy (=sensitivity), the test device cancorrectly determine an affected patient to be positive. With the degreeof accuracy (=specificity), the test device can correctly determine anunaffected person to be negative. Those degrees of accuracy can bespecified at the time the test device is manufactured. Until now, afinal determination of a doctor on a test result has been made withreference to those indices in clinical tests.

In contrast to this, for the positive or negative result shown by thetest device, there are indices of a positive predictive value and anegative predictive value that serve as indices representing aprobability on whether a patient is actually affected with a disease ornot.

The positive predictive value and the negative predictive value are veryimportant indices, which represent a probability of a test result, for adoctor who uses the test device in clinical practice to determine adiagnosis of a disease. The reason why it is important will be describedlater. The positive predictive value and the negative predictive valuecan be calculated from the sensitivity and the specificity of the testdevice, and a prevalence rate. Conversely, in the case where theprevalence rate varies from hour to hour in infectious diseases and thelike, the values of those indices also vary from hour to hour.

In the present technology, the prevalence rate, which varies from hourto hour, is adequately handled to assist a doctor to determine a moredefinite diagnosis using a test terminal in pandemic of an infectiousdisease, for example. This is one object to develop this test system.

In other words, examples in which information related to infection isprovided by public institutions are already found, but it has beenimpossible to immediately provide detailed information to correspond toeach test device or each patient. Immediately providing detailedinformation in such a manner is also one object to develop this testsystem.

[Regarding Prevalence Rate]

Here, the prevalence rate and indices related to the prevalence ratewill be described simply. FIG. 1 shows a state where a clinical test ofa certain disease is performed by a certain test method. Here, thenumber of persons who apply to a case (true positive) is “a”, in which apositive result is obtained by a test device and a doctor makes a finaldetermination for a certainty that the patient is affected with adisease. Further, the number of persons who apply to a case (falsepositive) is “c”, in which a positive result is obtained by the testdevice but the doctor makes a final determination that the patient isnot affected with the disease.

Furthermore, the number of persons who apply to a case (false negative)is “b”, in which a negative result is obtained by the test device butthe doctor makes a final determination that the patient is affected withthe disease. Moreover, the number of persons who apply to a case (truenegative) is “d”, in which a negative result is obtained by the testdevice and the doctor makes a final determination that the patient isnot affected with the disease.

From the definition of the figure, it is found that the prevalence rateis obtained by an expression (a+b)/(a+b+c+d). Further, the definitionsof indices related to the prevalence rate (positive, negative, positiverate, negative rate, positive predictive value, negative predictivevalue, number of diseases, number of non-diseases, total number,sensitivity, specificity, and accuracy) are as shown in the figure. Itshould be noted that in the case where there are a plurality of diseasesor test methods, a table like this figure can be created for eachcombination of the diseases and the test methods.

Hereinbefore, the prevalence rate and the indices related to theprevalence rate have been described.

[Relationship Between Prevalence Rate, and Positive Predictive Value andNegative Predictive Value]

Next, a relationship between the prevalence rate, and the positivepredictive value and the negative predictive value will be described.

First, according to Bayes' theorem, a probability (odds) that a patientis subjected to a certain test and determined to be actually affectedwith a disease is represented as the following mathematical expression(1), using pretest odds in which a positive result is obtained in a testbefore the test is performed, and a likelihood ratio.

posttest odds=pretest odds×likelihood ratio  (1)

Further, odds (Ω) are represented by the following mathematicalexpression (2) using a probability (p).

Ω=p/(1−p)  (2)

It should be noted that from the mathematical expression (2), theprobability (p) is represented by the following mathematical expression(3) using the odds (Ω).

p=Ω/(1+Ω)  (3)

Further, posttest positive odds (that will be described later) arerepresented by the following mathematical expression (4) using thepretest odds and a positive likelihood ratio (that will be describedlater).

posttest positive odds=pretest odds×positive likelihood ratio  (4)

Furthermore, posttest negative odds (that will be described later) arerepresented by the following mathematical expression (5) using thepretest odds and a negative likelihood ratio (that will be describedlater).

posttest negative odds=pretest odds×negative likelihood ratio  (5)

Here, the definition expressions of relationships among other indicesare also represented by the following mathematical expressions (6) to(11).

$\begin{matrix}{{{prevalence}\mspace{14mu}{rate}} = {{number}\mspace{14mu}{of}\mspace{14mu}{disease}\text{s/t}{otal}\mspace{14mu}{number}}} & (6) \\\left. {{{pretest}\mspace{14mu}{odds}} = {{{prevalence}\mspace{14mu}{rat}\text{e/(}1} - {{prevalence}\mspace{14mu}{rate}}}} \right) & (7) \\\left. {{{posttest}\mspace{14mu}{positive}\mspace{14mu}{odds}} = {{{positive}\mspace{14mu}{predictive}\mspace{14mu}{valu}\text{e/(}1} - {{positive}\mspace{14mu}{predictive}\mspace{14mu}{value}}}} \right) & (8) \\\left. {{{posttest}\mspace{14mu}{negative}\mspace{14mu}{odds}} = {{{negative}\mspace{14mu}{predictive}\mspace{14mu}{valu}\text{e/(}1} - {{negative}\mspace{14mu}{predictive}\mspace{14mu}{value}}}} \right) & (9) \\{\left. {{{positive}\mspace{14mu}{likelihood}\mspace{14mu}{ratio}} = {{{sensitivit}\text{y/(}1} - {specificity}}} \right) = \left( {{number}\mspace{14mu}{of}\mspace{14mu}{true}\mspace{14mu}{positiv}\text{e/n}{umber}\mspace{14mu}{of}\mspace{14mu}{diseases}\text{)/}\left( {{number}\mspace{14mu}{of}\mspace{14mu}{false}\mspace{14mu}{positiv}\text{e/n}{umber}\mspace{14mu}{of}\mspace{14mu}\text{non-diseases}} \right)} \right.} & (10) \\{{{negative}\mspace{14mu}{likelihood}\mspace{14mu}{ratio}} = \left( {{1 - {{sensitivity}\text{)/s}{pecificity}}} = \left( {{number}\mspace{14mu}{of}\mspace{14mu}{false}\mspace{14mu}{negativ}\text{e/n}{umber}\mspace{14mu}{of}\mspace{14mu}{diseases}\text{)/}\left( {{number}\mspace{14mu}{of}\mspace{14mu}{true}\mspace{14mu}{negativ}\text{e/n}{umber}\mspace{14mu}{of}\mspace{14mu}\text{non-diseases}} \right)} \right.} \right.} & (11)\end{matrix}$

By the above mathematical expressions, the positive predictive value andthe negative predictive value are represented by the followingmathematical expressions (12) and (13) using the sensitivity, thespecificity, and the prevalence rate.

$\begin{matrix}\left. {{{positive}\mspace{14mu}{predictive}\mspace{14mu}{value}} = {{{sensitivity} \times {prevalence}\mspace{14mu}{rat}\text{e/(}{sensitivity} \times {prevalence}\mspace{14mu}{rate}} + {\left( {1 - {{prevalence}\mspace{14mu}{rate}}} \right)\left( {1 - {specificity}} \right)}}} \right) & (12) \\{{{negative}\mspace{14mu}{predictive}\mspace{14mu}{value}} = {{specificity} \times \left( {1 - {{prevalence}\mspace{14mu}{rate}\text{)/(}{specificity} \times \left( {1 - {{prevalence}\mspace{14mu}{rate}}} \right)} + {{prevalence}\mspace{14mu}{rate} \times \left( {1 - {sensitivity}} \right)}} \right)}} & (13)\end{matrix}$

It should be noted that the mathematical expressions described above maybe represented using the probability (p) or using the odds (Ω), andinformation to be obtained are synonymous.

Hereinbefore, the fact that each of the positive predictive value andthe negative predictive value can be represented as a function of theprevalence rate has been described.

Next, the relationship between the positive predictive value and thenegative predictive value, and the prevalence rate will be described inmore details. FIG. 2 is a graph showing a relationship between thepositive predictive value and the negative predictive value, and theprevalence rate. It should be noted that in a test device to be used inthis test, a sensitivity is 90%, and a specificity is 90%.

From the graph, for example, when the prevalence rate is 50%, that is,when the number of patients who are actually affected with a disease isapproximately half the number of patients who are subjected todiagnoses, the positive predictive value and the negative predictivevalue are each approximately 90%, and it is found that a test result canbe trusted.

However, for example, when the prevalence rate is approximately 5%, thatis, when 100 persons are subjected to diagnoses and there areapproximately 5 persons affected with a disease, the positive predictivevalue is approximately 30%, and it is found that a test result isdifficult to trust.

Though not shown in this graph, for example, even in the case of using atest device having a sensitivity of 99% in order to increase the degreeof accuracy of diagnosis, if the prevalence rate is extremely low, thepositive predictive value falls below 50% and the reliability of thetest result is reduced.

As described above, the prevalence rate is very important index for adoctor who makes a diagnosis of a disease based on clinical testresults.

Hereinbefore, the relationship between the prevalence rate, and thepositive predictive value and the negative predictive value has beendescribed.

[Regarding Presentation of Treatment Plan Based on Positive PredictiveValue and Negative Predictive Value]

Next, presentation of a treatment plan based on the positive predictivevalue and the negative predictive value will be described. Here,description will be given on a configuration to present a test plan anda treatment plan to be adopted next after a test based on the calculatedpositive predictive value and negative predictive value in the testterminal described above.

(Regarding Important Index in MRSA Infection)

Here, infection of MRSA (Methicillin-resistant Staphylococcus aureus)will be exemplified.

In order to prevent nosocomial infection, it is necessary toindividually manage MRSA-infected patients. In the individualmanagement, expense for infection prevention measures such as expensefor a private room, and burdens of healthcare professionals, such ashand-washing and wearing of aprons, are required.

In order to reduce those burdens as much as possible, it is important tomake a correct diagnosis on whether such a patient is really affectedwith MRSA or not. Examples of the test method include a genetic test,immunoassay, and a cultivation test. If the presence or absence ofinfection of MRSA is tested by those tests and a MRSA-uninfected personcan be correctly diagnosed to be MRSA negative, the number of affectedpersons to be individually managed can be reduced, and the expense forinfection prevention measures can be reduced. From this viewpoint, thenegative predictive value is important regarding MRSA infection.

(Regarding Example of Plan Presented in MRSA Infection)

Next, a specific example will be given regarding a plan to be adopteddepending on the levels of the sensitivity, the specificity, theprevalence rate, and the negative predictive value.

For example, when a test method in which the sensitivity is 85% and thespecificity is 90% is used, if the prevalence rate is 40% or less, thenegative predictive value of this test is 90% or more. The test terminalthus presents a recommendation for implementation of the test.

If the prevalence rate is more than 40%, the negative predictive valueof this test is less than 90%. The test terminal thus does not recommendthis test, and presents a recommendation for implementation of anothertest method with a higher sensitivity or a recommendation forimplementation of individual management of patients without performing atest.

Similarly, when a test method in which the sensitivity is 90% and thespecificity is 90% is used, if the prevalence rate is 50% or less, thenegative predictive value is 90% or more. The implementation of the testis thus recommended. If the prevalence rate is above 50%, the negativepredictive value is less than 90%. The test terminal thus does notrecommend implementation of this test, and presents a recommendation forimplementation of another test method with a higher sensitivity or arecommendation for implementation of individual management of patients.

Similarly, when a test method in which the sensitivity is 95% and thespecificity is 90% is used and when the prevalence rate is 66.7% orless, the negative predictive value is 90% or more. The implementationof the test is thus recommended. If the prevalence rate is more than66.7%, the negative predictive value is less than 90%. The test terminalthus does not recommend the test, and recommends another test methodwith a higher sensitivity or recommends individual management ofpatients.

(Regarding Recommendation of Test Method Based on Prevalence Rate)

Next, description will be given on what test method can be recommendedto a doctor by the test terminal based on the prevalence rate.

As described above, there is a predetermined relationship among thesensitivity, the specificity, the prevalence rate, and the negativepredictive value. In this regard, in the case where the negativepredictive value is intended to be 90% or more when the prevalence rateis 30%, it is found that a test method with the sensitivity of 77% andthe specificity of 90% or more only needs to be used.

Further, in the case where the negative predictive value is intended tobe 90% or more when the prevalence rate is 40%, it is found that a testmethod with the sensitivity of 85% and the specificity of 90% or moreonly needs to be used.

Furthermore, in the case where the negative predictive value is intendedto be 90% or more when the prevalence rate is 50%, it is found that atest method with the sensitivity of 90% and the specificity of 90% ormore only needs to be used.

Moreover, in the case where the negative predictive value is intended tobe 90% or more when the prevalence rate is 60%, it is found that a testmethod with the sensitivity of 93% and the specificity of 90% or moreonly needs to be used.

When this is applied to a specific case example, for example, the testterminal recommends a test method to be executed to a doctor as follows.Specifically, when the prevalence rate is 30%, use of animmunochromatographic test kit is recommended. The immunochromatographictest kit provides a low sensitivity but can suppress costs. When theprevalence rate is 50%, it is conceived that a genetic test kit that isexpensive but provides a high sensitivity or a cultivation test thattakes a long test time but provides a high sensitivity is recommended.

It should be noted that in the test system, a list of test methods thatare feasible in healthcare facilities in which tests are performed maybe held, and an optimum test method may be recommended to a doctor basedon the sensitivity, the specificity, the prevalence rate, and thenegative predictive value.

Hereinbefore, the configuration to present a test plan and a treatmentplan to be adopted next after a test based on the calculated positivepredictive value and negative predictive value in the test terminal hasbeen described.

[Regarding Specific Example of Prevalence Rate]

Next, a specific example of the prevalence rate described above will bedescribed. Here, an example will be described in which the prevalencerate changes depending on eras, regions, periods, ages, communities, andthe like.

(Example in which Prevalence Rate Changes Depending on Eras)

First, description will be given on a state where the prevalence rate ofdrug-resistant bacteria changes with the lapse of eras. Description hereis based on information on a morbidity change in drug-resistantbacteria, which is created by CDC (Centers for Disease Control andPrevention) of the United States of America. It should be noted that themorbidity and the prevalence rate are similar indices. Here, themorbidity is replaced with the prevalence rate for description.

In the United States of America, the proportion of Methicillin-resistantStaphylococcus aureus (MRSA) to Staphylococcus aureus is approximately5% in 1980, whereas the proportion changes to approximately 30% in 1990and approximately 50% in 2000. Similarly, the proportion ofVancomycin-resistant Enterococcus (VRE) to enterococci or the proportionof Fluoroquinolone-resistant Pseudomonas Aeruginosa (FQRP) topneumococci is 2% or less in 1990, whereas the proportion changes to 20%or more in 2000.

As described above, since the prevalence rate of drug-resistant bacteriachanges with lapse of eras, in order to enhance the degree of accuracyof a diagnosis, it is important to grasp the latest prevalence rate whena test is performed.

(Example in which Prevalence Rate Changes Depending on Regions(Countries))

Next, description will be given on a state where the prevalence rate ofdrug-resistant bacteria changes depending on regions (countries).Description here is based on materials of Euro Surveillance 2008 Nov. 20Volume 13, Issue 47 by European Antimicrobial Resistance SurveillanceSystem (EARSS). The materials show prevalence rates of drug-resistantbacteria on a country-by-country basis in Europe.

According to the materials of EARSS, the proportion of VRE toenterococci in 2007 is 30% or more in Ireland and Greece, 30 to 20% inthe United Kingdom, 20 to 10% in Czech Republic, 10 to 5% in Italy,Germany, and Portugal, 5 to 1% in Spain, France, Switzerland, Austria,and other countries, and 1% or less in Norway, Sweden, Finland, Pohland,and other countries.

As described above, since the prevalence rate also differs depending onregions and countries, in order to enhance the degree of accuracy of adiagnosis, it is important to grasp the latest prevalence rate of aregion where a test is performed.

Example 1 of Prevalence Rate of Influenza Virus

Next, description will be given on a state where the prevalence rate ofinfluenza virus fluctuates depending on periods and regions. Here,materials of Tokyo Metropolitan Institute of Public Health are used. Thematerials show the number of patients affected with influenza persentinel on a period basis and on a yearly basis.

According to the materials, the prevalence rate of influenza virus tendsto be low in June and July, whereas it tends to be high in February andMarch every year. However, in such a tendency, an epidemic start perioddiffers yearly, and its prevalence rate also largely differs. Further,as in the epidemic of pandemic strains (H1pdm) in 2009, the prevalencerate is sometimes increased in October, November, and December in whichthe epidemic does not occur in usual years.

Further, though not shown in the figures here, also in Infectious AgentsSurveillance Report (IASR, http://idsc.nih.go.jp/iasr/influ.html) ofNational Institute of Infectious Diseases, the number of cases ofinfection of pathogens in sentinels and other healthcare facilities,health departments, and the like is reported as a report of infectiousdisease surveillance from prefectural and municipal public healthinstitutes. According to the IASR, it is found that there is adifference in period and region of influenza epidemic. Additionally,there is a difference in period and region of influenza epidemicdepending on types of influenza viruses.

As described above, the prevalence rate of the influenza virus largelydiffers depending on years, periods, and types of viruses. Therefore, inorder to enhance the degree of accuracy of a diagnosis, a test systemthat can collect prevalence rate information very quickly andcontinuously when a test is performed is effective.

Example 2 of Prevalence Rate of Influenza Virus

Next, description will be given on a state where the prevalence rate ofinfluenza virus fluctuates depending on ages of patients or communitiesto which patients belong. Here, materials of the Ministry of Health,Labour and Welfare and the Koriyama health department of Nara Prefectureare used. The materials show the number of estimated consultations ofpersons on an age group basis in the infectious disease surveillance ofthe Ministry of Health, Labour and Welfare.

According to the materials, the prevalence rate of influenza virus inthe ages of 0 to 14, particularly in the ages of 5 to 9 tends to behigher than the other age groups. In other words, the prevalence ratelargely changes depending on the age groups.

As a result, it is important to determine a test result using an optimumprevalence rate according to the age of a subject being tested.

Further, the prevalence rate also differs depending on communities towhich patients belong. For example, the “Status of Pandemic Influenza inthe season of 2012 to 2013”, which is reported by the Koriyama healthdepartment of Nara Prefecture, provides a report example in which theprevalence rate of influenza virus in early elementary school years ishigh. For example, there is provided a report example in which theprevalence rate in the first year grade of a certain elementary schoolin the season of 2011 to 2012 is 30% or more.

On the other hand, according to the hospital admission surveillance andthe infectious disease surveillance of the Ministry of Health, Labourand Welfare, the prevalence rate of influenza-like virus in the seasonof 2011 to 2012 in Japan is estimated as 16,480,000 persons. Assumingthat the population of Japan is 128 million persons based on the resultof the census in 2010, the prevalence rate of influenza virus is 12.9%at a maximum, which differs from the example of Nara Prefecture. Inother words, this suggests that the prevalence rate of influenza virusdiffers depending on communities.

As a result, it is important to determine a test result using an optimumprevalence rate according to communities to which subjects being testedbelong.

Hereinbefore, the specific example of the prevalence rate has beendescribed.

[Regarding Configuration of Test System]

Next, the overall configuration of a test system to which the presenttechnology is applied will be described. In a test system using thepresent technology, a client server configuration is adopted. FIG. 3 isa diagram showing a configuration in which a test system 10 that adoptsthe present technology connects test terminals 20 with a test server 40via a network. As shown in this figure, in the test system 10 thatadopts the present technology, a plurality of test terminals 20 servingas clients are dispersedly disposed in countries, regions, andfacilities and are connected to the test server 40 via the network 30.

(Reason why Client Server Configuration is Adopted)

First, the reason why the test system 10 that adopts the presenttechnology has to have a client server configuration will be described.

As described above, using the latest prevalence rate is one of points inthe present technology. As found from the definition described above,regarding this prevalence rate, as the total number of tests becomeslarger, the degree of accuracy of a calculated prevalence rate becomeshigher.

Further, in order to increase the total number of tests, there are anapproach to performing many tests in one test terminal and an approachto collecting test results from many test terminals. In the presenttechnology, in order to achieve an approach to collecting test resultsfrom many test terminals, a client server configuration formed of thetest server 40 and the plurality of test terminals 20 is adopted as aconfiguration of the test system 10.

Adopting this configuration allows the number of test terminals 20serving as clients to be increased as much as possible. This can improvethe degree of accuracy of the prevalence rate provided from the testserver 40 to the test terminals 20.

(Regarding Configuration of Test Server 40)

Next, a hardware configuration of the test server 40 will be described.The test server 40 may be configured by dedicated hardware or softwareor may be configured by a general computer. FIG. 4 is a block diagram ofa case where the test server 40 is configured by a general computer.

As shown in the figure, the test server 40 includes a CPU (CentralProcessing Unit, control unit, first control unit) 41, a ROM (Read OnlyMemory) 42, a RAM (Random Access Memory) 43, an operation input unit 44,a network interface unit (communication unit, first communication unit)45, a display unit 46, and a storage unit 47, and those blocks areconnected to one another via a bus 48.

The ROM 42 fixedly stores a plurality of programs and data such asfirmware to execute various types of processing. The RAM 43 is used as awork area of the CPU 41 and temporarily holds an OS (Operating System),various applications being executed, and various types of data beingprocessed.

The storage unit 47 is, for example, an HDD (Hard Disk Drive), a flashmemory, or another non-volatile memory such as a solid-state memory. Inthe storage unit 47, a database 47 a that will be described later isstored in addition to the OS, the various applications, and the varioustypes of data.

The network interface unit 45 is connected to the network 30 forexchanging information with the test terminals 20, and collectsinformation from the test terminals 20 or provides processed informationto the test terminals 20.

The CPU 41 develops a program corresponding to a command provided fromthe operation input unit 44, in a plurality of programs stored in theROM 42 and the storage unit 47, to the RAM 43 and appropriately controlsthe display unit 46 and the storage unit 47 according to the developedprogram.

Further, the CPU 41 updates the database 47 a based on informationcollected from the test terminals 20 via the network 30 and the networkinterface unit 45. The CPU 41 then extracts necessary information fromthe database 47 a based on a condition specified by a demand of theinformation received from the test terminals 20, counts and returns theinformation to the test terminals 20.

The operation input unit 44 is, for example, a pointing device such as amouse, a keyboard, a touch panel, or another operating device.

The display unit 46 is, for example, a liquid crystal display, an EL(Electro-Luminescence) display, a plasma display, or a CRT (Cathode RayTube) display. The display unit 46 may be incorporated in the testserver 40 or may be externally connected.

Hereinbefore, the configuration of the test server 40 has beendescribed.

(Regarding Database 47A)

Next, a configuration example of records stored in the database 47 awill be described. FIG. 5 is a diagram showing an example of fields(items) in each record that configures the database 47 a. It should benoted that those items are referred to as test information items.

In the example of this diagram, the items of Instrument ID, Patient ID,Sample ID, Date (date of test), Address (address, administrativedistrict), Country, Gender, Age, Assay result (test result), Diagnosis(diagnosis of doctor) are arranged from the left.

Those items are examples. More items may be provided, or those items maybe narrowed down to less items depending on the types of information tobe collected, extracted, and counted by the test server 40. In the casewhere the number of items is increased, for example, Disease ID, Testmethod ID, and the like may be added. The addition of those Disease IDand Test method ID allows this test system to correspond to a pluralityof disease or a plurality of test methods. It should be noted that amethod of using those items is described later.

(Regarding Configuration of Test Terminal 20)

Next, a hardware configuration of the test terminal 20 will bedescribed. The test terminal 20 may be configured by dedicated hardwareor software or may be configured by a test device and a generalcomputer. FIG. 6 is a block diagram of a case where the test terminal 20is configured by a test device and a general computer.

As shown in the figure, the test terminal (communication terminal) 20includes a CPU (control unit, second control unit) 21, a ROM 22, a RAM23, an operation input unit (input unit) 24, a network interface unit(communication unit, second communication unit) 25, a display unit 26, astorage unit 27, and a test device 28, and those blocks are connected toone another via a bus 29. It should be noted that description ofconstituent elements having the same functions as those of the testserver 40 will be omitted.

The network interface unit 25 is connected to the network 30 forexchanging information with the test server 40, and transmitsinformation to the test server 40 or receives information processed inthe test server 40.

The CPU 21 presents information, which is received from the test server40 via the network 30 and the network interface unit 25, to a user or adoctor via the display unit 26, or performs various types of processingbased on the received information. The various types of processing willbe described later. Further, the CPU 21 transmits a test result in thetest device 28 or a final diagnosis of a doctor who made a diagnosis ofa disease to the test server 40 via the network 30 and the networkinterface unit 25.

The test device 28 is a device with which a disease is actually tested.A test result is read by the CPU 41, and then presented to a doctor whoperformed the test via the display unit 26 or transmitted to the testserver 40 via the network 30. It should be noted that in the case wherea test kit is used as the test device, the test terminal 20 mayautomatically read a test result, or a test result may be input to thetest terminal 20 manually.

Hereinbefore, the configuration of the test terminal 20 has beendescribed.

[Regarding Processing Flow of Test System 10]

Next, a processing flow performed in the test system 10 will bedescribed. First, the overall flow will be described. Next, details ofindividual processing will be described. Lastly, a processing flow willbe described as an application example or a modified example.

(Overall Processing Flow)

First, an overall processing flow in the test system 10 will bedescribed. FIG. 7 is a flowchart for describing the overall processingflow in the test system 10.

First, the CPU 41 of the test server 40 counts and calculates aprevalence rate for one disease and one test method using the database47 a in the test server 40 (Step S10). It should be noted that detailsof this count and calculation processing are described later. It shouldbe noted that the count and calculation processing may be started on acertain-time-period basis (for example, every hour or every day) or maybe started using, as a trigger, a request (demand) from a test terminal20 with which a doctor will implement a test.

Next, in the test terminal 20 with which a test will be implemented, theCPU 21 of the test terminal 20 downloads the prevalence rate calculatedin the test server 40 (Step S20). It should be noted that downloadingmay be performed by pull communication from the test terminal 20 or bypush communication from the test server 40.

Next, in the test terminal 20 that downloads the prevalence rate, theCPU 21 calculates a positive predictive value and a negative predictivevalue according to the mathematical expressions (12) and (13) describedabove (Step S30). It should be noted that the mathematical expressionsare shown below again. Further, it is assumed that the values of thesensitivity and the specificity are preliminarily held by the testterminal 20.

$\begin{matrix}\left. {{{positive}\mspace{14mu}{predictive}\mspace{14mu}{value}} = {{{sensitivity} \times {prevalence}\mspace{14mu}{rat}\text{e/(}{sensitivity} \times {prevalence}\mspace{14mu}{rate}} + {\left( {1 - {{prevalence}\mspace{14mu}{rate}}} \right)\left( {1 - {specificity}} \right)}}} \right) & (12) \\{{{negative}\mspace{14mu}{predictive}\mspace{14mu}{value}} = {{specificity} \times \left( {1 - {{prevalence}\mspace{14mu}{rate}\text{)/(}{specificity} \times \left( {1 - {{prevalence}\mspace{14mu}{rate}}} \right)} + {{prevalence}\mspace{14mu}{rate} \times \left( {1 - {sensitivity}} \right)}} \right)}} & (13)\end{matrix}$

It should be noted that the positive predictive value and the negativepredictive value may be directly obtained from expressions a/(a+c) andd/(b+d), respectively, without using the prevalence rate.

Next, in the test terminal 20 that downloads the prevalence rate, theCPU 21 presents the prevalence rate, the positive predictive value, andthe negative predictive value to a user or a doctor via the display unit26 (Step S40).

Next, in the test device 28 of the test terminal 20 that downloads theprevalence rate, a test is implemented according to an instruction ofthe user (Step S50). Details of the processing of the test will bedescribed later.

Next, in the test terminal 20 that downloads the prevalence rate, theCPU 21 uploads a diagnosis etc. (test information), which is input tothe test terminal 20, to the test server 40 (Step S60). It should benoted that the test information uploaded here may be the same as theitems that configure records of the database 47 a described above.Further, uploading may be directly performed from the test terminal 20or may be performed via a laboratory information system (LIS) or asmartphone. Details of an uploading method will be described later.

Next, in the test server 40, the CPU 41 registers the uploaded testinformation such as a diagnosis in the database 47 a (Step S70).

After the registration in the database 47 a in Step S70, the processingreturns to Step S10 and the processing described above is repeated.

Hereinbefore, the overall processing flow in the test system 10 has beendescribed.

(Regarding Count and Calculation Processing of Prevalence Rate)

Next, details of the processing to count and calculate the prevalencerate described above will be described. FIG. 8 is a flowchart fordescribing the details of the processing to count and calculate theprevalence rate.

First, the CPU 41 of the test server 40 clears the total number ofdiagnoses and the number of diseases to zero for initialization. Thetotal number of diagnoses and the number of diseases are variables usedfor count up at the time of count (Step S11).

Next, the CPU 41 determines whether all records of the database 47 a areread or not (Step S12). It should be noted that whether all records areread or not is determined in the case where the database 47 a isconstituted by records related to one disease and one test method. Inthe case where the records related to a plurality of diseases and aplurality of test methods are included in the database 47 a, whether allrecords related to diseases or test methods to be counted are read ornot may be determined.

At this moment, since all the records of the database 47 a are not yetread (N of Step S12), the CPU 41 then reads one record from the database47 a (Step S13).

Next, the CPU 41 counts up the total number of diagnoses by 1 (StepS14).

Next, the CPU 41 determines whether “Diagnosis” of the doctor, which isone field of the read record, is positive or not (Step S15).

Only in the case where the result is positive (Y of Step S15), the CPU41 counts up the number of diseases by 1 (Step S16).

In the case where the result is negative in Step S15 (N of Step S15) ofafter the number of diseases is counted up in Step S16, the CPU 41returns to the processing of Step S12 and continues the processing.

In Step S12, in the case where all records of the database 47 a are read(Y of Step S12), the CPU 41 then calculates a prevalence rate from thetotal number of diagnoses and the number of diseases according to themathematical expression (6) (Step S17). It should be noted that themathematical expression (6) is as follows.

prevalence rate=number of diseases/total number  (6)

Hereinbefore, the details of the processing to count and calculate theprevalence rate have been described. It should be noted that in theabove description, all the records in the database 47 a are to beprocessed, but the present technology is not limited thereto. Forexample, the following configuration may be provided: an item of “dataregistration date and time” is provided to a record, and only a recordregistered within a certain period of time in the past is processed.

Further, in the above description, the records in the database 47 a arecounted so as to obtain the prevalence rate, but the present technologyis not limited thereto. The following configuration may be provided: avalue of the prevalence rate is acquired from outside the test system10. An acquisition method may be a method passing through the network 30or may be a method of extracting a numerical value of the prevalencerate from a research paper and the like and manually inputting thenumerical value to the test server 40. When a numerical value from aresearch paper and the like is manually input to the test server 40, itis desirable to have a standard to simplify the input.

(Regarding Implementation of Test)

Next, details of the implementation of the test described above will bedescribed. FIG. 9 is a flowchart for describing the details of theimplementation of a test.

First, the user inputs patient information via the operation input unit24 or the like of the test terminal 20 (Step S51).

Next, according to an instruction of the user or the CPU 21 of the testterminal 20, a test is executed in the test device 28 (Step S52).

Next, the CPU 21 reads a test result in the test device 28 and presentsthe test result to the user or the doctor via the display unit 26 (StepS53).

Next, the doctor inputs a final diagnosis to the test terminal 20, basedon the prevalence rate, the positive predictive value, the negativepredictive value, and the test result that are displayed in the testterminal 20 (Step S54). When the diagnosis is input, the doctor candetermine a final diagnosis with higher degree of accuracy by referringto the prevalence rate, the positive predictive value, and the negativepredictive value.

Hereinbefore, the details of the implementation of a test have beendescribed.

(Modified Example 1 Calculation of Positive Predictive Value Etc. InTest Server 40)

In the above description, the processing has been described in which thetest terminal 20 has information of the test terminal's sensitivity andspecificity, and downloads only the prevalence rate from the test server40 to calculate the positive predictive value and the negativepredictive value on the test terminal 20 side.

In contrast to this, in a modified example to be described here, thetest server 40 holds information on the sensitivities and specificitiesof various types of test devices 28. It should be noted that for valuesof the sensitivity and the specificity used here, values that arespecific to the test devices 28 and provided as performance of the testdevices 28 by manufacturers of the test devices 28 can be used. Beforedemanding information such as the prevalence rate, the test terminal 20notifies the test server 40 of the device ID and the like of the testterminal 20. The test server 40 calculates a positive predictive valueand a negative predictive value in the test server 40 using the valuesof the sensitivity and the specificity, which are associated with thenotified device ID. The test terminal 20 downloads the prevalence rate,the positive predictive value, and the negative predictive value fromthe test server 40 and presents them to the user.

Adopting this configuration allows the test terminal to omit theprocessing to calculate the positive predictive value and the negativepredictive value. Further, the sensitivity and the specificity of eachtest terminal 20 can be arbitrarily adjusted on the test server 40 side.

(Modified Example 2 Download and Presentation of More Information)

In the above description, only the prevalence rate is downloaded orthree of the prevalence rate, the positive predictive value, and thenegative predictive value are downloaded from the test server 40 to thetest terminals 20. In contrast to this, in a modified example to bedescribed here, more information may be downloaded and presented to theuser. Examples of the more information include the total number ofdiagnoses and the number of diseases. By presentation of suchinformation to the user, the adequacy of the calculated positivepredictive value and negative predictive value can be determined.

(Modified Example 3 Improvement of Predictive Value by AdequateSensitivity/Specificity)

In the above description, the sensitivity and the specificity areuniquely determined in the test device 28. In contrast to this, in amodified example to be described here, a configuration will be describedin which the sensitivity and the specificity are changed based on theelapsed time from the development of a disease such as an infectiondisease.

For example, in the case of a respiratory tract infection disease, it isknown that the number of pathogens in the nasal cavity or pharynxfluctuates with the elapsed time from the development of a disease.Along with the change in the number of pathogens, the sensitivity andthe specificity of a test also fluctuate. As a result, based on theelapsed time from the development of a disease of a patient, the degreeof accuracy of the positive predictive value and negative predictivevalue to be obtained can be improved using adequate values of thesensitivity and the specificity.

FIG. 10 is a flowchart for describing processing using a sensitivity anda specificity that are based on an elapsed time from the development ofa disease, in the above-mentioned processing to implement a test.

First, the user inputs patient information to the test terminal 20 (StepS51 a). When the patient information is input, an elapsed time from thedevelopment of a disease is also input.

Next, the CPU 21 of the test terminal 20 acquires a sensitivity and aspecificity based on the input elapsed time from the development of adisease (Step S51 b). It should be noted that a sensitivity and aspecificity to be acquired may be preliminarily held in the testterminal 20 or may be downloaded from the test server 40 that holds thesensitivity and specificity.

It is desirable to have a standard for facilitating the acquisition ofthe sensitivity and the specificity. For example, it may be possible todisplay a bar code on the package of a diagnosis kit and scan the barcode, to capture a specific sensitivity and specificity into the testsystem 10.

Further, it may also be possible to establish a database on thesensitivity and the specificity of each test device 28 in the testsystem 10 and acquire, based on a medical-device identification numberof the test device 28, the sensitivity and specificity of the testdevice 28, a sensitivity and specificity on an disease-development-timebasis, a sensitivity and specificity on a patient's age basis, and thelike.

Next, the CPU 21 calculates a positive predictive value and a negativepredictive value using the acquired prevalence rate, sensitivity, andspecificity (Step S51 c).

Next, the CPU 21 presents the calculated positive predictive value andnegative predictive value to the user (Step S51 d). It should be notedthat when the positive predictive value and the negative predictivevalue are presented to the user, the acquired prevalence rate,sensitivity, and specificity may also be presented together.

Processing of Step S51 d and steps subsequent thereto are the same asthose described above, and thus description thereof will be omitted.

Hereinbefore, the configuration has been described in which thesensitivity and the specificity are changed based on the elapsed timefrom the development of a disease such as an infection disease.

(Modified Example 4 Combination of Plurality of Tests (Integration ofTest Results))

In the above description, the configuration in which one test isexecuted as a test of a disease has been described. In contrast to this,in a modified example to be described here, a configuration will bedescribed in which various types of tests are executed and results ofthe tests are integrated, to output a final test result (which is not afinal diagnosis). In the configuration of this modified example, varioustypes of tests may be executed, and only in the case where results inall the tests are positive, a final test result may be considered to bepositive. This allows the degree of accuracy of likelihood (sensitivityand specificity) to be improved, and also allows the degree of accuracyof a positive predictive value and a negative predictive value, whichare finally calculated, to be improved.

FIG. 11 is a flowchart for describing processing in which various testsare performed and results of the tests are comprehensively used in theabove-mentioned processing to implement a test.

First, the user inputs patient information (Step S51). This step is thesame as the step described above.

Next, the test terminal 20 executes various types of tests (in thisexample, three types of tests A, B, and C) (Step S52 a, 52 b, and 52 c).The tests may be executed simultaneously and parallel or executedsequentially one by one. It should be noted that the determination oftest results is performed after all test results are obtained.

Next, the CPU 21 of the test terminal 20 determines whether all the testresults of the respective tests are positive or not (determines testresults showing the presence of disease) (Step S52 d).

In the case where all the results are positive (Y of Step S52 d), theCPU 21 determines a final test result to be positive (Step S52 e). Here,although a final test result is determined to be positive in the casewhere all the test results are positive, there may be a case where theCPU 21 determines a final test result to be positive when all theresults are not necessarily positive, depending on conditions such asthe sensitivity of each test.

In the case where any of the results is negative (N of Step S52 d), theCPU 21 determines a final test result to be negative (determines testresults showing the absence of disease) (Step S52 f).

Processing of Step 53 and steps subsequent thereto are the same as thosedescribed above, and thus description thereof will be omitted. It shouldbe noted that the following processing is performed using the “finaltest result” obtained here as the “test result” described above.

Hereinbefore, the configuration in which various types of tests areexecuted, results of the tests are integrated, and a final test resultis output has been described.

(Modified Example 5 Combination of Plurality of Tests (StepwiseExecution of Tests))

In the above modified example in which a plurality of tests arecombined, the configuration in which after all the tests are performed,all test results are integrated for processing has been described. Incontrast to this, in a modified example to be described here, aplurality of tests are executed one by one, and each time one testresult is obtained, whether to continue a test or not is determined. Inthis modified example, stepwise execution of tests allows the degree ofaccuracy of a final diagnosis based on the prevalence rate to beimproved.

In the configuration of this modified example, for example, in the casewhere tests A, B, and C are sequentially performed and when results ofthe respective tests are determined to be positive, the odds that apatient is determined to be truly positive are increased. According toposttest odds (posttest positive odds and posttest negative odds) of therespective tests, a trade-off between costs of subsequent tests and sideeffects that may be caused can be considered. Depending oncircumstances, it is also possible to make a choice to perform nosubsequent tests, determine the patient to be positive, and advance thetreatment of the patient.

FIG. 12 is a flowchart for describing processing in which a plurality oftests are executed one by one and each time one test result is obtained,whether the test is continued or not is determined, in theabove-mentioned processing to implement a test. Here, a configuration inwhich tests A, B, and C are sequentially performed as a plurality oftests is provided.

First, the CPU 21 of the test terminal 20 calculates pretest odds basedon the prevalence rate downloaded from the test server 40, using themathematical expression (7) (Step S49 a).

Next, the CPU 21 presents the calculated pretest odds to the user or thedoctor via the display unit 26 (Step S53 a).

Next, the doctor determines whether the test A is needed to be executedor not (Step S55).

In the case where the execution is determined to be unnecessary (N ofStep S55), no tests are performed.

In the case where the execution of the test A is determined to benecessary (Y of Step S55), the operation input unit 24 then receives aninput of patient information from the user (Step S51).

Next, the test device 28 of the test terminal 20 executes the test A(Step S52 a).

Next, the CPU 21 calculates posttest positive odds in the test A basedon a result of the test A and on pretest odds and a positive likelihoodratio related to the test A, using the mathematical expression (4) (StepS49 b). It should be noted that the positive likelihood ratio is usedhere, but the present technology is not limited thereto. A configurationin which at least one of the positive likelihood ratio and the negativelikelihood ratio is used may be adopted.

Next, the CPU 21 presents the posttest positive odds to the user (StepS53 b).

Next, the doctor determines whether an additional test is needed or notby referring to the presented posttest positive odds (Step S56).

In the case where an additional test is determined to be unnecessary (Nof Step S56), the test B and the test C are not performed. Theprocessing proceeds to the input of the diagnosis of the doctor (StepS54).

In the case where an additional test is determined to be necessary (Y ofStep S56), the CPU 21 then causes the test device 28 to execute the testB (Step S52 b).

Next, the CPU 21 calculates posttest positive odds in the test B, as inStep S49 b, based on the test result of the test A, a test result of thetest B, and pretest odds and a positive likelihood ratio related to thetest B (Step S49 c).

Next, the CPU 21 presents the calculated posttest positive odds to theuser or the doctor (Step S53 c).

Next, the doctor determines whether a further additional test is neededor not (Step S57).

In the case where a further additional test is determined to beunnecessary (N of Step S57), the test C is not performed. The processingproceeds to the input of the diagnosis of the doctor (Step S54).

In the case where a further additional test is determined to benecessary (Y of Step S57), the CPU 21 then causes the test device 28 toexecute the test C (Step S52 c).

Next, the CPU 21 presents a test result of the test C to the user (StepS53 d).

Next, the doctor inputs a final diagnosis to the test terminal 20 byreferring to the test result (Step S54).

Hereinbefore, the processing to execute a plurality of tests one by oneand determine whether to continue a test or not each time one testresult is obtained has been described.

(Modified Example 6 Narrowing-Down of Target Data Based on Attribute ofTest Terminal)

In the above description, the prevalence rate is counted and calculatedfor all records stored in the database 47 a, that is, all the testresults. In contrast to this, in a modified example to be describedhere, a configuration will be described in which test results to be thebasis for counting and calculating the prevalence rate are narrowed downbased on an attribute of the test terminal 20 (terminal attributeinformation).

In an example described here, test results used to calculate theprevalence rate are narrowed down to only results of tests performed inan administrative district (for example, Tokyo) to which a test terminal20 that demands a prevalence rate belongs, or to only test resultsacquired within the range of a physical distance (for example, 50 km)from the test terminal 20. In other words, the narrowing-down isperformed based on the attribute of the test terminal 20. It should benoted that the narrowing-down used here refers to using only testresults matched with a certain condition to count the prevalence rate.

As described above, various types of narrowing-down are performed whenthe prevalence rate is calculated. This allows more adequate informationto be provided to the individual test terminals 20.

FIG. 13 is a flowchart for describing processing to count and calculatethe prevalence rate after narrowing down count target data based on anadministrative district and a physical distance, in the above-mentionedprocessing to count and calculate the prevalence rate.

First, the CPU 41 of the test server 40 acquires, from a test terminal20 to be an information-provided destination of the prevalence rate orthe like, an administrative district to which the test terminal 20belongs, a current position, and a physical distance (radius) of adesired range (Step S9 a).

Next, the CPU 41 clears the total number of diagnoses and the number ofdiseases in an identical administrative district, and the total numberof diagnoses of test results in the range of a specified distance andthe number of diseases, to zero for initialization (Step S11 a). Thetotal number of diagnoses and the number of diseases in an identicaladministrative district, and the total number of diagnoses of testresults in the range of a specified distance and the number of diseasesare variables used for count up at the time of count.

Next, the CPU 41 determines whether all records of the database 47 a areread or not (Step S12).

At this moment, since all the records of the database 47 a are not yetread (N of Step S12), the CPU 41 then reads one record from the database47 a (Step S13).

Next, the CPU 41 determines whether the administrative district of theread record is identical to the administrative district of the testterminal 20 or not (Step S18 a). It should be noted that the item“Administrative District” used here can be derived as a part of the item“Address” in the database 47 a.

In the case where the administrative district of the read record isidentical to the administrative district of the test terminal 20 (Y ofStep S18 a), the CPU 41 then counts up the total number of diagnoses inthe identical administrative district by 1 (Step S14 a).

Next, the CPU 41 determines whether “Diagnosis” of the doctor, which isone field of the read record, is positive or not (Step S15).

Only in the case where the result is positive (Y of Step S15), the CPU41 counts up the number of diseases in the identical administrativedistrict by 1 (Step S16 a).

In the case where the administrative district of the read record isdifferent from the administrative district of the test terminal 20 inStep 18 a (N of Step S18 a), in the case where the result is negative inStep S15 (N of Step S15), or after the number of diseases is counted upin Step S16 a, the CPU 41 advances the processing to Step S18 b andcontinues the processing.

Next, the CPU 41 determines whether a distance between a position atwhich a test in the read record is performed and the current position ofthe test terminal 20 falls within the specified range or not (Step S18b).

In the case where the distance falls within the specified range (Y ofStep S18 b), the CPU 41 then counts up the total number of diagnoseswithin the specified distance by 1 (Step S14 b).

Next, the CPU 41 determines whether “Diagnosis” of the doctor, which isone field of the read record, is positive or not (Step S15).

Only in the case where the result is positive (Y of Step S15), the CPU41 counts up the number of diseases within the specified distance by 1(Step S16 b).

In the case where the distance does not fall within the specified rangein Step 18 b (N of Step S18 b), in the case where the result is negativein Step S15 (N of Step S15), or after the number of diseases is countedup in Step S16 b, the CPU 41 returns the processing back to Step S12 andcontinues the processing.

In Step S12, in the case where all the records of the database 47 a arecompletely read (Y of Step S12), according to the mathematicalexpression (6), the CPU 41 then calculates the prevalence rate in theidentical administrative district from the total number of diagnoses andthe number of diseases in the identical administrative district, andcalculates the prevalence rate within the specified range from the totalnumber of diagnoses and the number of diseases within the specifiedrange (Step S17 a).

Hereinbefore, the configuration in which test results to be the basisfor counting and calculating the prevalence rate are narrowed down basedon the attribute of the test terminal 20 has been described.

(Modified Example 7 Narrowing-Down of Target Data Based on PatientAttribute)

In the above modified example in which narrowing-down is performed, theconfiguration has been described in which test results to be the basisfor counting and calculating the prevalence rate are narrowed down basedon the attribute of the test terminal 20. In contrast to this, in amodified example to be described here, a configuration will be describedin which test results to be the basis for counting and calculating theprevalence rate are narrowed down based on the attribute of a patientwho is subjected to a test (patient attribute information), instead ofthe attribute of the test terminal 20.

In the example described here, test results to be counted are narroweddown based on the gender or age of a patient who is subjected to a testwith the test terminal 20 that demands a prevalence rate, to count andcalculate the prevalence rate. However, an example of narrowing-down tobe described here is different from the above-mentioned narrowing-downbased on the attribute of the test terminal 20, and is exactly a sortingon an attribute content basis.

For example, in narrowing-down by gender, count is not performed basedon only gender identical to that of the patient, but performed based onmale and female, and respective values are held. In the case where theprevalence rate of a gender is required, the test server 40 collects theprevalence rates of males and females and provides the prevalence ratesto the test terminal 20.

It should be noted that as in the example of the above-mentionednarrowing-down based on the attribute of the test terminal 20, it isneedless to say that the following configuration is adopted: genderinformation of a patient who is subjected to a test is first acquired,records of the gender of male only are extracted from the database 47 afor counting, and then only a prevalence rate of male is calculated. Inthe case of calculating a prevalence rate related to an attribute thatis less common and is not demanded by the test terminal 20 frequently,it is effective to perform extraction related to that attribute eachtime such a demand is generated.

As described above, performing various types of narrowing-down when theprevalence rate is calculated allows more adequate information to beprovided to individual patients.

FIG. 14 is a flowchart for describing processing to count and calculatethe prevalence rate after narrowing down count target data based ongender and an age category (for example, categories on a ten-year basis,such as the ages of 0 to 9 and the ages of 10 to 19) of patients, in theprocessing to count and calculate the prevalence rate described above.It should be noted that in the description of this flowchart, processingsimilar to the above-mentioned narrowing-down based on the attribute ofthe test terminal 20 will not be described.

First, the CPU 41 of the test server 40 clears variables of the totalnumber of diagnoses and the number of diseases, on an attribute categorybasis, to zero for initialization (Step S11 b). The variables are usedfor count up at the time of count.

Next, the CPU 41 determines whether all records of the database 47 a areread or not (Step S12).

At this moment, since all the records of the database 47 a are not yetread (N of Step S12), the CPU 41 then reads one record from the database47 a (Step S13).

Next, the CPU 41 counts up the total number of diagnoses and the numberof cases determined to be positive, on a gender category basis (Step S18a).

Next, the CPU 41 counts up the total number of diagnoses and the numberof cases determined to be positive, on an age category basis (Step S18b). The CPU 41 then returns the processing back to Step S12 andcontinues the processing.

In Step S12, in the case where all the records of the database 47 a arecompletely read (Y of Step S12), the CPU 41 then calculates theprevalence rate on an attribute category basis, from the total number ofdiagnoses and the number of diseases on an attribute category basis(Step S17 b).

It should be noted that here, the gender and the age are exemplified asthe attributes of patients, but the narrowing-down may be performedusing an attribute other than those above attributes. At that time, itis assumed that an item corresponding to that attribute is provided inthe records of the database 47 a and data corresponding to that item isaccumulated.

Examples of other attributes of patients include (1) medical interviewinformation, (2) current and past medication information, (3) previousdisease, (4) physical information such as a body temperature, a bloodpressure, and a body weight, (5) information on lifestyle habits, suchas an exercise volume, a volume or kinds of food, and a sleep length.

Further, examples of still other attributes of patients include (6)genotypes of germline and somatic line genes, including GenomicVariants, SNPs (Single Nucleotide Polymorphism), GWAS (Genome-wideAssociation Study), indel (insertion-deletion), CNV (Copy NumberVariation), mRNA (messenger RNA), Epigenetics, miRNA (micro-RNA), andthe like of the genes.

Further, attributes such as (7) microbial flora (intestinal bacteria andthe like) of patients and (8) race can also be used.

Hereinbefore, the configuration in which test results to be the basisfor counting and calculating the prevalence rate are narrowed down basedon the attribute of a patient who is subjected to a test, instead of theattribute of the test terminal 20.

(Modified Example 8 Case where Narrowing-Down Based on Patient Attributeis not Enabled)

In the modified example in which the above-mentioned narrowing-down isperformed, the narrowing-down is performed using the attribute of thetest terminal 20 or the attribute of patients. In contrast to this, in amodified example to be described here, one of solutions to a case wherethe number of target test results becomes insufficient as a result ofthe narrowing-down and a meaningful prevalence rate cannot be derivedfrom the count of the test results will be described.

In a solution described here, a prevalence rate that is obtained fromall cases in the database 47 a before narrowing-down is performed iscorrected based on correction information acquired from outside the testsystem 10, and thus a prevalence rate in the case of narrowing-downunder a target condition is calculated.

It should be noted that in the following description, a geneticpolymorphism among attributes of patients will be described as acondition for narrowing-down.

FIG. 15 is a flowchart of processing to calculate, in the case where thenumber of registered patients is small in the database 47 a andnarrowing-down according to a genetic polymorphism is meaningless, aprevalence rate for that genetic polymorphism by correcting the overallprevalence rate using a predetermined sensitivity. In other words, whenthe number of patients having the genetic polymorphism is notsufficiently registered in the test server 40 but there is sensitivitycorrection information for calculating a prevalence rate for thatgenetic polymorphism, the prevalence rate for that genetic polymorphismis calculated from the overall prevalence rate by correction.

First, the CPU 41 of the test server 40 acquires genetic polymorphisminformation of patients, which is input to the test terminal 20 as aninformation-provided destination (Step S9 b). It should be noted thatthe genetic polymorphism information of patients may be directly inputto the test terminal 20 or may be acquired from outside, such as anotherserver, based on a Patient ID received from the test terminal.

Next, the CPU 41 clears variables of the total number of diagnoses andthe number of diseases in an identical genetic polymorphism to zero forinitialization (Step S11 c). The variables are used for count up at thetime of count.

Next, the CPU 41 determines whether all records of the database 47 a areread or not (Step S12).

At this moment, since all the records of the database 47 a are not yetread (N of Step S12), the CPU 41 then reads one record from the database47 a (Step S13).

Next, the CPU 41 determines whether a genetic polymorphism of the readrecord and a genetic polymorphism acquired from the test terminal 20 areidentical or not (Step S18 c).

In the case where the genetic polymorphisms are identical to each other(Y of Step S18 c), the CPU 41 then counts up the total number ofdiagnoses in the identical genetic polymorphism by 1 (Step S14 c).

Next, the CPU 41 determines whether “Diagnosis” of the doctor, which isone field of the read record, is positive or not (Step S15).

Only in the case where the result is positive (Y of Step S15), the CPU41 counts up the number of diseases in the identical geneticpolymorphism by 1 (Step S16 c).

In the case where the genetic polymorphisms are different from eachother in Step 18 c (N of Step S18 c), in the case where the result isnegative in Step S15 (N of Step S15), or after the number of diseases iscounted up in Step S16 c, the CPU 41 returns the processing back to StepS12 and continues the processing.

In Step S12, in the case where all the records of the database 47 a arecompletely read (Y of Step S12), the CPU 41 then determines whether thetotal number of diagnoses in the identical genetic polymorphism issufficient or not (Step S19 a).

In the case where the total number is sufficient (Y of Step S19 a), theCPU 41 calculates a prevalence rate in an identical genetic polymorphismbased on the total number of diagnoses and the number of diseases in theidentical genetic polymorphism (Step S17 b).

In the case where the total number is not sufficient (N of Step S19 a),the CPU 41 then determines whether there is information for prevalencerate correction, which corresponds to the genetic polymorphism ofpatients (Step S19 b).

In the case where there is no information for prevalence rate correction(N of Step S19 b), the CPU 41 determines that a calculation of aprevalence rate corresponding to the genetic polymorphism of patients isimpossible, and then returns an error (Step S19 c).

In the case where there is information for prevalence rate correction (Yof Step S19 b), the CPU 41 then calculates a prevalence rate (generalprevalence rate) in the case where test results are not narrowed down toan identical genetic polymorphism (Step S21).

Next, the CPU 41 corrects the calculated general prevalence rate usingthe information for prevalence rate correction (sensitivityinformation), to calculate the prevalence rate in the geneticpolymorphism of patients (Step S17 c).

As described above, even if the number of patients having thetransmitted genetic polymorphism is not sufficiently registered in thetest server 40, in the case where there is sensitivity information usedfor prevalence rate correction, which corresponds to that geneticpolymorphism, the corrected prevalence rate can be returned to the testterminal.

It should be noted that in the above description, the correction valuefor correcting a general prevalence rate is acquired from outside, butthe present technology is not limited thereto. A prevalence ratecorresponding to each category of a terminal attribute and a patientattribute may be acquired from outside. For example, prevalence rateinformation on a gender or age category basis may be acquired fromoutside in the format of XML (eXtended Markup Language) or the like.

Next, a configuration of a test server 40 a used in this modifiedexample will be described. FIG. 16 is a block diagram showing aconfiguration example of the test server 40 a that can correct theprevalence rate using the sensitivity information described above. Thedifference from the test server 40 described above is an external datainterface unit 49 additionally provided.

The sensitivity information used for correction based on a disease and agenetic polymorphism is input by the user via the operation input unit44 or acquired from a memory card or the like storing the sensitivityinformation, via the external data interface unit 49. It should be notedthat the sensitivity information may be acquired via the networkinterface unit 45 over the network 30.

Acquiring the sensitivity information for correction allows the testserver 40 a to correct a prevalence rate and provide a prevalence ratecorresponding to a specific genetic polymorphism during operation of aservice for providing a prevalence rate and the like.

(Modified Example 9 Correction of Prevalence Rate by Weighting)

In the above description, the configuration has been described in whichthe weight of one test result is counted as 1 (the number of positivecases is simply counted) when the count for calculating a prevalencerate is performed. In contrast to this, in a modified example to bedescribed here, the following configuration will be described in whichthe number of counted-up positive cases is weighted for correction, topredict a true prevalence rate, in consideration of an environmentalcondition where a test is performed (for example, immunizationpenetration rate in a specific region). It should be noted that theweighting is performed by multiplication of a coefficient according to apredetermined condition, for example.

FIG. 17 is a flowchart of processing to perform weighting correction onthe prevalence rate (diagnosis prevalence rate) calculated by the countof the database 47 a in a certain administrative district, based on theimmunization penetration rate in that administrative district, topredict a true prevalence rate. It should be noted that in theprocessing described here, the following mathematical expression (14) isassumed to be established using an immunization penetration rate f(k).

(predicted true prevalence rate)

=f(k)×(diagnosis prevalence rate)  (14)

Further, a specific value of the immunization penetration rate f(k) isassumed to be calculated based on a relationship between a trueprevalence rate determined in the past and a diagnosis prevalence ratecalculated in the past, using the mathematical expression (14).

First, the CPU 41 of the test server 40 acquires, from a test terminal20 to be a destination provided with information such as a prevalencerate, an administrative district to which that test terminal 20 belongs(Step S9 c).

Next, the CPU 41 clears the total number of diagnoses and the number ofdiseases in an identical administrative district to zero forinitialization (Step S11 d). The total number of diagnoses and thenumber of diseases in an identical administrative district are variablesused for count up at the time of count.

Next, the CPU 41 determines whether all records of the database 47 a areread or not (Step S12).

Here, the processing in Steps S13, S18 a, S14 a, S15, and S16 a, whichis performed by the time all the records of the database are completelyread, is the same as the processing described above and is forcalculating the total number of diagnoses and the number of diseases inan identical administrative district. Description of the processing willthus be omitted.

In Step S12, in the case where all the records of the database 47 a arecompletely read (Y of Step S12), the CPU 41 then calculates a prevalencerate (diagnosis prevalence rate) in the identical administrativedistrict from the total number of diagnoses and the number of diseasesin the identical administrative district according to the mathematicalexpression (6) (Step S17 d).

Next, the CPU 41 acquires an immunization penetration rate f(k) in theadministrative district to which the test terminal 20 belongs (Step S9d). It should be noted that the CPU 41 may preliminarily holdinformation of the immunization penetration rate f(k) in the test server40 or acquire the information from outside the test system 10. Further,the value of the immunization penetration rate f(k) may be updated inthe test server 40 as needed.

Next, the CPU 41 calculates a predicted true prevalence rate using themathematical expression (14) (Step S17 e). The predicted true prevalencerate calculated here is replaced with the prevalence rate describedabove, and the following processing is performed.

In the above description, the administrative district is used as acondition of weighting. In addition thereto, examples of characteristicsof the region as a condition of weighting include a country, apopulation, a population density, a position of the test terminal 20, adistance from the test terminal 20, uniqueness of an environment, apoverty level, a traffic situation around the test terminal 20, and apopulation change per day around the test terminal 20.

As found from the narrowing-down conditions used in the modified exampleof narrowing-down described above and the weighting conditions used inthe modified example of weighting described here, conditions such as anadministrative district can be used for narrowing-down and weighting.

It should be noted that in the above description, the weighting based onthe attribute of the test terminal 20 has been described, but theweighting may be performed based on patient attributes, for example,attributes such as a body temperature, a blood pressure, and a gene typeof a patient.

Hereinbefore, the configuration has been described in which the numberof counted-up positive cases is weighted for correction, to predict atrue prevalence rate, in consideration of an environmental conditionwhere a test is performed.

(Modified Example 10 Substitute Index of Prevalence Rate when Diagnosisis Unavailable (Part 1))

In the above description, it is assumed that a diagnosis of a doctor isalso certainly obtained in the past test result in order to calculate aprevalence rate. In contrast to this, in a modified example to bedescribed here, it is assumed that a final diagnosis of a doctor may notbe sometimes input when a test is performed with the test terminal 20.When a final diagnosis of a doctor may not be sometimes input, a blankis generated in the “Diagnosis” column of the database 47 a, and thedegree of accuracy of a prevalence rate to be collected and obtained isreduced. In this regard, in this modified example, an approximate indexto serve as a substitute for the prevalence rate is used instead of theprevalence rate.

For example, it is conceived that the positive rate is used as theapproximate index. As found from the following mathematical expression(15), the positive rate can be obtained from the number of test positivecases, which can be automatically acquired, when a test is performedwith the test terminal 20. For that reason, even in the case where arecord in which a final diagnosis of a doctor is not input exists in thedatabase 47 a and a calculation for a prevalence rate is notappropriately performed, using an index substituted by the positive rateallows an approximate value of an adequate prevalence rate to beprovided to the test terminal 20.

(positive rate)

=(number of test positive cases)/(total number of diagnoses)  (15)

It should be noted that a point of the following processing is that evenwhen the number of records in which a diagnosis of a doctor is input tothe database 47 a is insufficient, when the prevalence rate falls withina range capable of being replaced with the positive rate and the numberof records having positive test results is enough, the positive rate issubstituted for the prevalence rate.

FIG. 18 is a flowchart for describing processing using an approximateindex to be a substitute for the prevalence rate, instead of theprevalence rate.

First, the CPU 41 of the test server 40 clears the total number ofdiagnoses, the number of diseases, the number of diagnosis inputs, andthe number of positive test results to zero for initialization (Step S11e). The total number of diagnoses, the number of diseases, the number ofdiagnosis inputs, and the number of positive test results are variablesused for count up at the time of count.

Next, the CPU 41 determines whether all records of the database 47 a areread or not (Step S12).

At this moment, since all the records of the database 47 a are not yetread (N of Step S12), the CPU 41 then reads one record from the database47 a (Step S13).

Next, the CPU 41 then counts up the total number of diagnoses by 1 (StepS14).

Next, the CPU 41 determines whether the “Diagnosis” column of a doctor,which is one field of the read record, is filled or not (Step S18 d).

In the case where the “Diagnosis” column is filled (Y of Step S18 d),the CPU 41 then counts up the number of diagnosis inputs by 1 (Step S14d).

Next, the CPU 41 determines whether the “Diagnosis” of the doctor, whichis one field of the read record, is positive or not (Step S15).

In the case where the diagnosis is positive (Y of Step S15), the CPU 41counts up the number of diseases by 1 (Step S16).

In the case where the “Diagnosis” column is not filled in Step S18 d (Nof Step S18 d), in the case where the diagnosis is negative in Step S15(N of Step S15), or after the number of diseases is counted up in StepS16, the CPU 41 advances the processing to Step S18 e and continues theprocessing.

Next, the CPU 41 determines whether a test result of the test device 28is positive or not (Step S18 e).

Only in the case where the test result is positive (Y of Step S18 e),the CPU 41 counts up the number of positive test results by 1 (Step S16d).

In the case where the test result is negative in Step S18 e (N of StepS18 e) or after the number of diseases is counted up in Step S16 d, theCPU 41 returns the processing back to Step S12 and continues theprocessing.

In Step S12, in the case where all the records of the database 47 a arecompletely read (Y of Step S12), the CPU 41 calculates a prevalence ratefrom the total number of diagnoses and the number of diseases accordingto the mathematical expression (6) (Step S17).

Next, the CPU 41 determines whether the number of diagnosis inputs is apredetermined threshold value or more (Step S19 d).

In the case where the number of diagnosis inputs is a predeterminedthreshold value or more (Y of Step S19 d), the prevalence ratecalculated in Step S17 is considered as an adequate value and used inthe following processing.

In the case where the number of diagnosis inputs is less than apredetermined threshold value (N of Step S19 d), the prevalence ratecalculated in Step S17 is considered as an inadequate value for use inthe following processing. The CPU 41 then determines whether thecalculated prevalence rate falls within a range capable of beingreplaced with the positive rate or not (Step S19 e).

In the case where the calculated prevalence rate falls within a rangecapable of being replaced with the positive rate (Y of Step S19 e), theCPU 41 then determines whether the number of positive test results is apredetermined threshold value or more (Step S19 f).

In the case where the number of positive test results is a predeterminedthreshold value or more (Y of Step S19 f), the CPU 41 then calculates apositive rate using the mathematical expression (15) (Step S17 d).

Next, the CPU 41 substitutes the positive rate for the prevalence rate(Step S17 e). The positive rate is substituted for the value of theprevalence rate and used in the following processing.

It should be noted that in the case where the prevalence rate does notfall within a range capable of being replaced with the positive rate inStep S19 e (N of Step S19 e) and in the case where the number ofpositive test results is less than the predetermined threshold value inStep S19 f (N of Step S19 f), the CPU 41 determines that a substitutionof the positive rate for the prevalence rate is impossible, and thenreturns an error (Step S19 g).

Hereinbefore, the modified example in which an approximate index toserve as a substitute for the prevalence rate is used instead of theprevalence rate has been described.

(Regarding Relationship Between Prevalence Rate and Positive Rate inSpecific Sensitivity/Specificity)

Here, the fact that the relationship between the prevalence rate and thepositive rate changes based on the sensitivity and the specificity willbe described.

FIG. 19 is a graph showing the relationship between the prevalence rateand the positive rate when the sensitivity and the specificity arechanged. This figure shows the relationship between the prevalence rateand the positive rate, when the sensitivity and the specificity of thediagnosis device 28 are changed, by 5%, from 80% (line indicated by apositive rate 1) to 85% (line indicated by a positive rate 2), 90% (lineindicated by a positive rate 3), 95% (line indicated by a positive rate4), and 100% (line indicated by a positive rate 5).

As found from this graph, as the sensitivity/specificity are increased,the prevalence rate and the positive rate becomes matched, starting fromthe line of positive rate 1 in which the most mismatched relationshipbetween the prevalence rate and the positive rate is shown, and in theline of positive rate 5, the prevalence rate and the positive rate arematched. This shows that a positive rate based on a test result obtainedwith a diagnosis device having a higher sensitivity and specificityshows a value closer to the prevalence rate.

In other words, a positive rate based on a test result of a highersensitivity/high specificity test can present a more correct positivepredictive value and negative predictive value to the user.

Here, the positive predictive value and the negative predictive value inthe case where the positive rate is used instead of the prevalence ratewill be described. FIG. 20 is a graph showing a relationship between aprevalence rate or a positive rate as a substitute for the prevalencerate, and the positive predictive value and the negative predictivevalue.

Found in this graph is a relationship between a prevalence rate (orpositive rate), and a positive predictive value and negative predictivevalue to be calculated, when a test is performed with a test device 28having a sensitivity of 80% and a specificity of 80%. Lines of apositive predictive value 1 and a negative predictive value 1 representa positive predictive value and a negative predictive value that arecalculated using an original prevalence rate.

Lines of a positive predictive value 2 and a negative predictive value 2represent a positive predictive value and a negative predictive valuethat are calculated in the case of using a positive rate of another testwith a sensitivity of 80% and a specificity of 80% instead of theprevalence rate. Further, lines of a positive predictive value 3 and anegative predictive value 3 represent a positive predictive value and anegative predictive value that are calculated in the case of using apositive rate of another test with a sensitivity of 95% and aspecificity of 95% instead of the prevalence rate.

For example, a line of a positive predictive value 3(sensitivity/specificity of 95%) is approximate to the line of thepositive predictive value 1 as an original positive predictive value,compared with the line of the positive predictive value 2(sensitivity/specificity of 80%).

As described above, in the case where the positive rate is substitutedfor the prevalence rate, using a positive rate based on a diagnosisdevice having a higher sensitivity and a higher specificity leads toobtaining a positive predictive value and a negative predictive valuethat are more approximate to the positive predictive value and negativepredictive value calculated from the original prevalence rate. This ismore effective.

Hereinbefore, the fact that the relationship between the prevalence rateand the positive rate is changed based on the sensitivity and thespecificity has been described.

(Modified Example 11 Substitute Index of Prevalence Rate when Diagnosisis Unavailable (Part 2))

In the above modified example in which the positive rate is substitutedfor the prevalence rate, a positive rate obtained by a certain testmethod is used instead of a prevalence rate for the test method. Incontrast to this, in the case where the positive rate is substituted forthe prevalence rate as described above, using a positive rate based on adiagnosis device having a higher sensitivity and a higher specificityleads to obtaining a positive predictive value and a negative predictivevalue that are more approximate to the positive predictive value andnegative predictive value calculated from the original prevalence rate.This point is considered in this modified example. It should be notedthat a higher sensitivity and a higher specificity used here mean thatthey are sufficiently high enough to be credible, in other words, meanthat a predetermined value preliminarily demanded is satisfied.

In this regard, in the configuration of this modified example, on theassumption that test results based on a plurality of test methods usingdifferent sensitivities and specificities are stored in the database 47a, in the case where another index has to be substituted for theprevalence rate on a test method (first test method) using a lowsensitivity/specificity, a positive rate on a test method (second testmethod) using high numerical values of the sensitivity/specificity isused

As an example in which such a configuration may be adopted, a test ofinfluenza virus is exemplified. Examples of a test method for influenzavirus include immunochromatography of a low sensitivity/specificity anda PCR (Polymerase Chain Reaction) method of a highsensitivity/specificity.

In the case where the prevalence rate is difficult to calculate byimmunochromatography because a diagnosis of a doctor is not yet input, apositive rate by the PCR method is used as a substitute for theprevalence rate. This allows a calculation of values that are moreapproximate to the original positive predictive value and negativepredictive value even in the case where the prevalence rate is difficultto calculate by the immunochromatography.

It should be noted that the positive predictive value and the negativepredictive value to be calculated are represented by the followingmathematical expressions (16) and (17), using a sensitivity (sensitivityi) and a specificity (specificity i) by the immunochromatography and apositive rate (positive rate p) by the PCR method.

positive predictive value

=sensitivity i×positive rate p/(sensitivity i×positive ratep+(1−positive rate p)(1−specificity i))  (16)

negative predictive value

=specificity i×(1−positive rate p)/(specificity i×(1−positive ratep)+positive rate p×(1−sensitivity i))  (17)

Here, processing to count and calculate a prevalence rate in the casewhere the configuration of this modified example is adopted will bedescribed. FIG. 21 is a flowchart for describing processing to count andcalculate a prevalence rate in the case where the configuration of thismodified example is adopted.

It should be noted that this flowchart is almost the same as theflowchart in the modified example described above (identical theretofrom Step S11 e to Step S19 f), in which a positive rate of a certaintest method is substituted for a prevalence rate of the test method. Inthis regard, here, only a difference between the flowchart describedabove and a flowchart in this modified example will be described. Theflowchart in this modified example shows processing in which a positiverate of a test method is substituted for a prevalence rate of a certaintest method, the test method for the positive rate being more highlyaccurate (having higher sensitivity, higher specificity) than the testmethod for the prevalence rate.

In the case where the number of positive test results is a predeterminedthreshold value or more (Y of Step S19 f), the CPU 41 then acquires apositive rate based on a highly accurate test method (Step S17 f).

Next, the CPU 41 substitutes the positive rate for the prevalence rate(Step S17 e). The positive rate based on a more highly accurate testmethod is substituted for the value of the prevalence rate and used inthe following processing.

Hereinbefore, the modified example has been described in which apositive rate of a test method is substituted for a prevalence rate of acertain test method, the test method for the positive rate being morehighly accurate (having higher sensitivity, higher specificity) than thetest method for the prevalence rate.

It should be noted that in the case where results of a plurality of testmethods on one disease are registered in the database 47 a, asensitivity and a specificity may be subjected to weighted average toobtain a comprehensive sensitivity and specificity, based on the numberof registered records of the respective test methods. Further, asensitivity and a specificity of one test method may be applied to alltest methods registered in the database 47 a. Further, test methods maybe grouped and weighting may be performed for each of the groups, toobtain a comprehensive sensitivity and specificity. Furthermore, anotherindex as a substitute for the prevalence rate has been described above,but a prevalence rate in an institution that is representative of itsregion can also be used instead of the prevalence rate based on thedatabase 47 a.

(Modified Example 12 Presentation of Effectiveness on Implementation ofTest)

In the above configuration, the test terminal 20 presents information toa doctor who performs a test, the information being the prevalence rate,the positive predictive value, and the negative predictive value, thatis, being a reference for the doctor who makes a final diagnosis. Incontrast to this, in a modified example to be described here, forexample, the test terminal 20 determines whether a calculated positivepredictive value is a realistic value or not (evaluates theeffectiveness) and if it is realistic (effective), the test terminal 20recommends a doctor to perform a test.

It is useful when the doctor determines whether to execute a test or notto present, before the doctor executes a test, a probability (positivepredictive value) that a positive result is obtained in a test and istruly positive and a probability (negative predictive value) that anegative result is obtained in a test and is truly negative, to thedoctor.

Tests are always performed depending on a trade-off between side effectsdue to the execution of tests and costs of tests. For example, in thecase where the prevalence rate is extremely low and the calculatedpositive predictive value is also too low to be realistic, a choice toperform no tests can be made.

As described above, in the case where the positive predictive value istoo low to be realistic, the test terminal 20 can recommend execution ofno tests, or in the case where the positive predictive value issufficiently high, the test terminal 20 can recommend execution oftests, before performing tests.

FIG. 22 is a flowchart of processing to recommend execution of tests orexecution of no tests depending on the level of the calculated positivepredictive value.

First, the CPU 41 of the test server 40 counts and calculates aprevalence rate for one disease and one test method using the database47 a in the test server 40 (Step S10).

Next, in the test terminal 20 with which a test will be implemented, theCPU 21 of the test terminal 20 downloads the prevalence rate calculatedin the test server 40 (Step S20).

Next, in the test terminal 20 that downloads the prevalence rate, theCPU 21 calculates a positive predictive value and a negative predictivevalue (Step S30).

Next, the CPU 21 presents the prevalence rate, the positive predictivevalue, and the negative predictive value to a user or a doctor via thedisplay unit 26 (Step S40).

Next, the CPU 21 acquires a threshold value A of the positive predictivevalue, with which implementation of a test does not become realistic(Step S41).

Next, the CPU 21 acquires a threshold value B of the positive predictivevalue, with which implementation of a test becomes realistic (Step S42).It should be noted that the threshold value A and the threshold value Bmay be preliminarily held in the test terminal 20, may be downloadedfrom the test server 40, or may be acquired from outside by anothermethod.

Next, the CPU 21 determines whether the calculated positive predictivevalue is the threshold value A or less (Step S43).

In the case where the calculated positive predictive value is thethreshold value A or less (Y of Step S43), the CPU 21 displays arecommendation for implementation of no tests to the user or doctor viathe display unit 26 (Step S44).

In the case where the calculated positive predictive value is above thethreshold value A (N of Step S43), the CPU 21 then determines whetherthe calculated positive predictive value is the threshold value B ormore (Step S45).

In the case where the calculated positive predictive value is thethreshold value B or more (Y of Step S45), the CPU 21 displays arecommendation for implementation of a test to the user or doctor viathe display unit 26 (Step S46).

After the recommendation is displayed in Step S44 or Step S46 or in thecase where the calculated positive predictive value is less than thethreshold value B in Step S45 (N of Step S45), the CPU 21 then allowsthe doctor to determine whether to implement a test or not (Step S47).

In the case where the doctor determines implementation of a test andinstructs the test terminal 20 to implement a test (Y of Step S47), atest is then implemented in the test device 28 of the test terminal 20(Step S50).

Next, the CPU 21 uploads a diagnosis etc., which is input to the testterminal 20, to the test server 40 (Step S60).

Next, in the test server 40, the CPU 41 registers the uploadedinformation such as a diagnosis in the database 47 a (Step S70).

After the registration in the database 47 a in Step S70 or in the casewhere it is determined in Step S47 that a test is not implemented (N ofStep S47), the processing returns back to Step S10 and the processingdescribed above is repeated.

Hereinbefore, the modified example has been described in which the testterminal 20 determines whether the calculated positive predictive valueis a realistic value or not, and if it is realistic, recommends thedoctor to implement a test.

(Modified Example 13 Prediction of Prevalence Rate in Region where Testis not Implemented)

In the above description, past test results in a region where a test isimplemented are counted, to calculate a prevalence rate in that region.In contrast to this, in a modified example to be described here, aprevalence rate in a region where a test is not previously implementedis predicted from prevalence rates calculated in other regions.

FIG. 23 is a diagram showing prevalence rates of a plurality of regionsfor which test results are already accumulated, and a state where aprevalence rate of a region where a test is not yet performed ispredicted in accordance with distances from the plurality of regions.

In this figure, tests have been implemented in the past in a city A, acity B, and a city C, for which test results are accumulated and aprevalence rate is calculated. In a city D, however, a test is not yetperformed, and a prevalence rate cannot be calculated based on the countof the past test results. Here, when a distance between the cities A andD is denoted as a distance AD, a distance between the cities B and D isdenoted as a distance BD, and a distance between the cities C and D isdenoted as a distance CD, it is assumed that the prevalence rates of thecities A, B, and C and a prevalence rate of the city D to be predictedhave a relationship shown by a mathematical expression in the figure.

The prevalence rates of the cities A, B, and C are obtained using thetest terminal 20, distances between the cities are input to the testterminal 20, and the test terminal 20 is caused to perform a calculationbased on the mathematical expression shown in the figure. This allowsthe prevalence rate of the city D to be predicted.

As described above, when a prevalence rate of a region where a test isnot implemented can be predicted, for example, a guideline showing aregion in which a mobile hospital or the like should perform a diagnosisor treatment next can be obtained.

It should be noted that here, a prevalence rate of a certain region isassumed to be inversely proportional to the square of a distance fromanother region. In addition thereto, the weighting correction may beperformed based on a factor having an influence on infection, that is,at least one of a traffic situation between cities or a distanceconsidering geographic features, a measurement time, a populationdensity, and a medical level.

Hereinbefore, the modified example has been described in which theprevalence rate of a region where a test is not previously performed ispredicted from prevalence rates calculated in other regions.

(Modified Example 14 Prediction of Future Prevalence Rate)

In the above description, the test system 10 provides a currentprevalence rate. In contrast to this, in a modified example to bedescribed here, a future predicted prevalence rate is also provided inaddition to the current prevalence rate. It should be noted thatprocessing to provide a future predicted prevalence rate may beperformed in the test server 40 or in the test terminal 20, or may beshared between them. Here, description will be given on the assumptionthat the processing is performed in the test server 40.

FIG. 24 is a flowchart for describing a processing flow to provide afuture predicted prevalence rate as well, in addition to a currentprevalence rate. In this processing, a future predicted prevalence rateafter a certain time period is calculated based on a change rate of theprevalence rate during a certain time period up to the present. Further,in this processing, when a predicted prevalence rate exceeds apredetermined threshold value, warning is displayed. It should be notedthat in this flowchart, only a part on processing to be described hereis shown, and the step of storing a past prevalence rate, which is to bea precondition for the processing, as history information in the storageunit 47 is not described.

First, the CPU 41 of the test server 40 calculates a current prevalencerate (Step S100). This processing is performed by the method describedabove.

Next, the CPU 41 reads a prevalence rate before a certain time period,from history information of the storage unit 47 (Step S101). “Beforecertain time period” means before 24 hours, for example.

Next, the CPU 41 calculates a change rate of a prevalence rate per unittime, based on the prevalence rate before a certain time period and thecurrent prevalence rate (Step S102). The change rate of the prevalencerate per unit time may be obtained by the following mathematicalexpression (18), for example.

(change rate of prevalence rate per unit time)

=((current prevalence rate)−(prevalence rate before 24 hours))/24  (18)

It should be noted that in order to reduce a potential of erroneousprocessing due to temporal fluctuation of a prevalence rate, thefollowing calculations may be performed:

(a) use an average of a plurality of prevalence rates that are acquiredon a finer time basis (for example, every one hour) to serve as acurrent prevalence rate or a prevalence rate before a certain timeperiod; and(b) calculate change rates on a shorter time basis and obtain an averageof those change rates.

Next, the CPU 41 calculates a predicted prevalence rate after a certaintime period, based on the current prevalence rate and the change rate ofthe prevalence rate per unit time (Step S103). For example, thepredicted prevalence rate after 24 hours may be calculated by thefollowing mathematical expression (19).

(predicted prevalence rate after 24 hours)

=(current prevalence rate)+(change rate per unit time)×24  (19)

Next, the CPU 41 determines whether the predicted prevalence rateexceeds a predetermined threshold value or not (Step S104).

Only in the case where the predicted prevalence rate exceeds apredetermined threshold value (Y of Step S104), the CPU 41 gives awarning of future infection spread to the user (Step S105). The warninggiven here may be given by any method. For example, the warning may bedisplayed on the display unit 26 of the test terminal 20 or may be givenvia e-mails, web pages on the Internet, and various types of SNS (SocialNetworking Service).

Hereinbefore, the modified example has been described in which thefuture predicted prevalence rate is also provided in addition to thecurrent prevalence rate.

(Modified Example 15 Optimization of Communication)

In the above description, the configuration has been described in whichinformation such as the prevalence rate is downloaded from the testserver 40 each time a test is performed on the test terminal 20 side. Incontrast to this, in this modified example, in order to reduce a load onthe test server 40 and reduce communication charges between the testserver 40 and the test terminals 20, the downloaded information such asthe prevalence rate is cached in the test terminal 20. The test terminal20 does not demand the test server 40 to provide information such as theprevalence rate each time a test is performed, but uses the informationsuch as the prevalence rate cached in the test terminal 20 during acertain time period.

In the configuration of this modified example, the whole processing isroughly divided into two processing groups according to a frequency ofthe processing. One processing group is for processing performed on apredetermined certain-time-period basis, from count of the prevalencerate in the test server 40 to downloading of the prevalence rate,caching of the prevalence rate, and the like. The other processing groupis for processing performed each time a test is performed, from readingof a cached prevalence rate and the like, to implementation of a testand reflection of a test result on the database 47 a. It should be notedthat the predetermined certain-time-period basis may be, for example, 30minutes, 3 hours, or one day.

25 is a flowchart showing processing on a predeterminedcertain-time-period basis and processing in each implementation of test.

The processing on a certain-time-period basis will be described first.It should be noted that Steps S10 to S30 are the same as those describedabove, and thus simply described.

First, the CPU 41 of the test server 40 counts and calculates aprevalence rate using the database 47 a in the test server 40 (StepS10).

Next, in the test terminal 20 in which information such as theprevalence rate is cached, the CPU 21 of the test terminal 20 downloadsthe prevalence rate calculated in the test server 40 (Step S20).

Next, in the test terminal 20 that downloads the prevalence rate, theCPU 21 calculates a positive predictive value and a negative predictivevalue (Step S30).

Next, the CPU 21 stores (caches) the downloaded prevalence rate and thecalculated positive predictive value and negative predictive value inthe storage unit 27 (Step S31).

After the processing of Step S31 is completed and a predeterminedcertain time period is elapsed, the processing is returned to Step S10and repeated.

Hereinbefore, the processing flow on a certain-time-period basis hasbeen described.

Next, the processing in each implementation of test will be described.It should be noted that Steps S40 to S70 are the same as those describedabove, and thus simply described.

First, the CPU 21 of the test terminal 20 reads a prevalence rate, apositive predictive value, and a negative predictive value from thecache in the storage unit 27 (Step S32).

Next, the CPU 21 presents the prevalence rate, the positive predictivevalue, and the negative predictive value to a user or a doctor via thedisplay unit 26 (Step S40).

Next, in the test device 28 of the test terminal 20, a test isimplemented according to an instruction of the user (Step S50).

Next, the CPU 21 uploads a diagnosis etc., which is input to the testterminal 20, to the test server 40 (Step S60).

Next, in the test server 40, the CPU 41 registers the uploadedinformation such as a diagnosis in the database 47 a (Step S70).

Hereinbefore, the processing flow in each implementation of test hasbeen described.

It should be noted that, as described above, in the case whereinformation displayed by the test terminal 20 is only the prevalencerate, the positive predictive value, and the negative predictive value,the configuration of this modified example is effective because thecache size of the storage unit 27 is suppressed to be low. Further, alsoin the case where the modified example described above in which targetdata is narrowed down by patient attributes is applied, it is effectivebecause of the same reason when the number of categories in a certainattribute is small. For example, in the case where a prevalence rate ona gender basis is cached, a prevalence rate to be stored includes onlytwo types.

As described above, using the configuration of the modified exampleallows a reduction of a load of the test server 40 or a reduction ofcommunication charges between the test server 40 and the test terminals20, as compared with a configuration in which the prevalence rate andthe like are counted in the test server 40 in each implementation oftest and a count result is downloaded.

It should be noted that in the configuration described above, the testterminal 20 downloads information such as a prevalence rate on acertain-time-period basis, but the present technology is not limitedthereto. For example, a configuration may be adopted, in which the testserver 40 distributes a count result such as a prevalence rate to thetest terminal 20 on a certain-time-period basis.

Hereinbefore, the modified example in which the downloaded informationsuch as a prevalence rate is cached in the test terminal 20 has beendescribed.

(Modified Example 16 Upload Form for Diagnosis Etc.)

In the above description, the configuration has been described in whichthe doctor inputs a final diagnosis etc. to the test terminal 20 and thetest terminal 20 uploads the final diagnosis etc. to the test server 40.In contrast to this, in a modified example to be described here, aconfiguration will be described in which information such as a diagnosisis uploaded to the test server 40 via a local system such as an LIS(Laboratory Information System) in a hospital or a cloud system on theInternet.

FIG. 26 is a diagram showing a configuration for uploading a diagnosisetc. using the LIS. The left part of the diagram shows a configurationin which a prevalence rate etc. are downloaded from the test server 40and directly uploads a diagnosis etc. from the test terminal 20 to thetest server 40. The configuration is described above. The center part ofthe diagram shows a configuration in which a doctor inputs a diagnosisto an LIS and the LIS uploads the diagnosis etc. to the test server 40.The right part of the diagram shows a configuration in which a doctorinputs a diagnosis to an LIS, the LIS transfers the diagnosis to thetest terminal 20, and the test terminal 20 uploads the diagnosis to thetest server 40.

It should be noted that though not shown in the figures, a configurationmay be adopted in which a doctor inputs a final diagnosis to a systemcapable of accessing a wide range, such as a cloud system over theInternet, using a smartphone or a tablet PC, for example. In this case,the diagnosis etc. are transferred from the cloud system to the testserver 40.

Hereinbefore, the modified example has been described in which theinformation such as a diagnosis is uploaded to the test server 40 viathe local system such as an LIS in a hospital or the cloud system overthe Internet.

(Modified Example 17 Recommendation for Medication)

In the above description, the configuration for presenting thecalculated prevalence rate, positive predictive value, negativepredictive value, and the like to a user or a doctor in the testterminal 20 has been described. In contrast to this, in a modifiedexample to be described here, the test terminal 20 recommends medicationto the user.

The recommendation for medication may be performed based on the level ofthe prevalence rate, the positive predictive value, and the negativepredictive value, based on a result of an implemented test, or based ona final diagnosis of the doctor.

It should be noted that the recommendation for medication specificallyrefers to display of, for example, the presence or absence of thenecessity of medication, a name of a medicine to be given, and a list ofmedicines to be medication candidates, on the display unit 26.

As a matter of course, in order to achieve the modified example, it isassumed that a knowledge base on diseases and medication is establishedin the test terminal 20, the test server 40, or outside the test system10.

Hereinbefore, the modified example in which the test terminal 20recommends medication to the user has been described.

(Modified Example 18 User Interface)

In the above description, the configurations for displaying, on thedisplay unit 26 of the test terminal 20, a general prevalence rate, aprevalence rate as a result of narrowing-down by an attribute, aprevalence rate as a result of weighting, a positive rate as asubstitute for the prevalence rate, recommendation for testimplementation/non-implementation, a predicted prevalence rate, arecommendation for medication, a recommendation for individualmanagement of patients, and the like have been individually described.In contrast to this, in a modified example to be described here, aconfiguration in which those displays are integrated will be described,for example.

FIG. 27 is a diagram showing a specific example in which a list of testmethods feasible by the test device 28 of the test terminal 20 ispresented on the test terminal 20 in addition to a name of a disease, aprevalence rate, a positive predictive value, and a negative predictivevalue, and a recommended test method is further displayed thereon.

In the figure, a PCR method is displayed with highlight together with arecommendation mark 26 a. Further, beside each name of a test method, aUI (User Interface) such as a test start button 26 b for instructing thetest terminal 20 to start testing directly from this display screen isdisplayed. It should be noted that a UI for instructing a start of atest may have a configuration to give an instruction by a tracingoperation or the like, in addition to a button.

Further, though not being a screen when information is presented, ascreen displaying UIs for performing the following operations may beprovided.

For example, a UI for giving an instruction to upload a test result tothe test server 40 may be displayed on a screen displayed when a test isended.

Further, instead of automatic uploading of a final diagnosis of a doctorto the test server 40, a UI for giving an instruction to upload adiagnosis to the test server 40 may be displayed on a screen indicatingthe input completion of the diagnosis.

Further, after the test is ended and a diagnosis etc. of the doctor areuploaded to the test server 40, a UI for giving an instruction to startthe next test may be displayed on the screen.

Further, a UI for transferring to a screen for viewing statisticalinformation such as the prevalence rate, the positive predictive value,and the negative predictive value may be provided on a screen displayedwhen the test is ended.

Further, after a final diagnosis of the doctor is input, it may bepossible to display a screen on which various conditions for treatmentdesired by patients are input to the test terminal 20, to introducehospitals and pharmacies corresponding to those conditions.

Further, after a final diagnosis of the doctor is input, it may bepossible to display a screen on which treatment cost paid for treatmentby a patient is input to the test terminal 20, to introduce a treatmentmethod corresponding to the amount of treatment cost.

Hereinbefore, the modified example in which the displays are integrated,and the like, have been described.

(Modified Example 19 Simplification of Test Terminal 20)

In the above description, the configuration has been described in whichthe test system 10 adopts a client server configuration and the testterminal 20 as a client and the test server 40 as a server shareprocessing. In contrast to this, in a modified example to be describedhere, a modified example will be described in which processing performedby the test terminal 20 is limited to minimum processing, and mostprocessing is performed by the test server 40.

For example, the function of the test terminal 20 may be limited todisplay of information received from a test server, input of patientinformation etc., transmission of input data to the test server 40,execution of a test, display of a test result and transmission thereofto the test server 40, and input of a diagnosis of a doctor andtransmission thereof to the test server 40.

The count, the calculation processing, the determination processing, andthe like are performed by the test server 40, and thus the configurationof the test terminal 20 can be simplified and costs can be reduced.

Further, in this configuration, in the case where a new function isadded, only the configuration of the test server 40 may be changed. Thetest terminal 20 does not need any change. Consequently, it is possibleto omit time and effort for modifying many test terminals 20.

Hereinbefore, the modified example has been described in which theprocessing performed by the test terminal 20 is limited to minimumprocessing, and most processing is performed by the test server 40.

[Summary of Configurations of the Present Technology]

Here, the outline of the configurations and functions of the test system10, the test server 40, and the test terminal 20 according to thepresent technology will be summarized.

The test server 40 according to the present technology includes thenetwork interface unit 45 and the CPU 41. The network interface unit 45communicates with the plurality of test terminals 20 via the network 30,the plurality of test terminals 20 each being connectable to a testdevice capable of executing a test on the presence or absence of adisease and each being capable of inputting a diagnosis on the presenceor absence of the disease, the diagnosis being related to the test andmade by a doctor. The CPU 41 acquires at least one of a result of thetest and the diagnoses as a test information item from each of theplurality of test terminals 20 via the network interface unit 45, causesthe storage unit 47 to store the plurality of acquired test informationitems therein, performs statistical processing on the plurality ofstored test information items, and causes the network interface unit 45to return a result of the statistical processing according to a demandgiven from each of the plurality of test terminals 20 before the doctormakes a diagnosis.

The test terminal 20 of the present technology includes the networkinterface unit 25, the operation input unit 24, and the CPU 21. Thenetwork interface unit 25 communicates with the test server 40 via thenetwork 30, the test server 40 collecting a plurality of sets of atleast one of a result of a test on the presence or absence of a diseaseand a diagnosis on the presence or absence of the disease as testinformation items, and providing a result of statistical processingperformed on the plurality of collected test information items, thediagnosis being related to the test and made by a doctor. The operationinput unit 24 receives an input from a user or the doctor. The CPU 21causes the network interface unit 25 to transmit a demand of the resultof the statistical processing to the test server 40, causes the testdevice 28 to execute the test, presents the result of the statisticalprocessing and a result of the executed test to the user, the result ofthe statistical processing being received via the network interface unit25 from the test server 40, causes the user to input the diagnosis onthe executed test, using the operation input unit 24, and causes thenetwork interface unit 25 to transmit at least one of the result of theexecuted test and the input diagnosis as the test information item tothe test server 40.

The test system 10 of the present technology includes the test server 40and the plurality of test terminals 20. The test server 40 includes thenetwork interface unit 45 and the CPU 41. The network interface unit 45communicates with the plurality of test terminals 20 via the network 30.The CPU 41 acquires at least one of a result of a test on the presenceor absence of a disease and a diagnosis on the presence or absence ofthe disease as a test information item from each of the plurality oftest terminals 20 via the network interface unit 45, the diagnosis beingrelated to the test and made by a doctor, causes the storage unit 47 tostore the plurality of acquired test information items therein, performsstatistical processing on the plurality of stored test informationitems, and causes the network interface unit 45 to return a result ofthe statistical processing according to a demand given from each of theplurality of test terminals 20 before the doctor makes a diagnosis. Theplurality of test terminals 20 each include the network interface unit25, the operation input unit 24, and the CPU 21. The network interfaceunit 25 communicates with the test server 40 via the network 30. Theoperation input unit 24 receives an input from a user or the doctor. TheCPU 21 causes the network interface unit 25 to transmit the demand ofthe result of the statistical processing to the test server 40, causes atest device to execute the test, presents the result of the statisticalprocessing and a result of the executed test to the user, the result ofthe statistical processing being received via the network interface unit25 from the test server 40, causes the user to input the diagnosis onthe executed test, using the operation input unit 24, and causes thenetwork interface unit 25 to transmit at least one of the result of theexecuted test and the input diagnosis as the test information item tothe test server 40.

[Regarding Effects by the Embodiment]

By the test system 10 according to this embodiment, for example, thefollowing effects can be obtained.

(1) Providing information to be indices for diagnosis, such as aprevalence rate, based on information acquired from many test terminals20 can improve the degree of accuracy of a final diagnosis made by adoctor.(2) Narrowing down and weighting information accumulated in the database47 a can further enhance the degree of accuracy of provided informationsuch as a prevalence rate.(3) Acquiring information that is not present in the test system 10 fromoutside can provide more useful information to a doctor, in addition toa prevalence rate and the like.(4) Change only on the test server 40 side can provide new informationbased on a new function to a doctor.(5) Unlike a typical test system, it is possible to cope with aninfection and the like with instantaneity.

[Supplementary Note]

In addition, the present technology is not limited to the aboveembodiments and can be variously modified without departing from thegist of the present technology as a matter of course.

[Another Configuration of the Present Technology]

It should be noted that the present technology can have the followingconfigurations.

(1) A test server, including:

-   -   communication unit that communicates with a plurality of        communication terminals via a network, the plurality of        communication terminals each being connectable to a test device        capable of executing a test on the presence or absence of a        disease and each being capable of inputting a diagnosis on the        presence or absence of the disease, the diagnosis being related        to the test and made by a doctor; and    -   a control unit that        -   acquires at least one of a result of the test and the            diagnosis as a test information item from each of the            plurality of communication terminals via the communication            unit,        -   causes a storage unit to store the plurality of acquired            test information items therein,        -   performs statistical processing on the plurality of stored            test information items, and        -   causes the communication unit to return a result of the            statistical processing according to a demand given from each            of the communication terminals before the doctor makes a            diagnosis.

(2) The test server according to (1), in which

-   -   the control unit causes the communication unit to return at        least one of a prevalence rate, a positive predictive value, and        a negative predictive value that are calculated as the result of        the statistical processing, based on the number of test        information items in which the result of the test and the        diagnosis are positive, the number of test information items in        which the result of the test is negative and the diagnosis is        positive, the number of test information items in which the        result of the test is positive and the diagnosis is negative,        and the number of test information items in which the result of        the test and the diagnosis are negative, in the plurality of        stored test information items.

(3) The test server according to (2), in which

-   -   the control unit causes the communication unit to return the        positive predictive value and the negative predictive value, in        addition to the prevalence rate, the positive predictive value        and the negative predictive value being calculated based on the        prevalence rate, a sensitivity of the test device, and a        specificity of the test device.

(4) The test server according to (2) or (3), in which

-   -   the control unit        -   acquires, from each of the communication terminals, an            elapsed time from the development of a disease of a patient            who is to be subjected to the test,        -   acquires a sensitivity and a specificity that correspond to            the elapsed time from the development of the disease, and        -   calculates the positive predictive value and the negative            predictive value based on the acquired sensitivity and            specificity.

(5) The test server according to any one of (1) to (4), in which

-   -   the control unit        -   causes the test device to execute various types of tests for            testing the disease, the test device being connected to each            of the communication terminals,        -   acquires results of the executed various types of tests from            the test device, and        -   determines a result of the test indicating the presence or            absence of the disease, based on the acquired results of the            various types of tests.

(6) The test server according to any one of (1) to (4), in which

-   -   the test device is capable of executing various types of tests,        and    -   the control unit calculates, after causing the test device to        execute one of the various types of tests, posttest odds in the        one test based on at least one of a positive likelihood ratio        and a negative likelihood ratio on the one test, transmits the        posttest odds to each of the communication terminals, and        acquires information on whether a subsequent test is performed        or not from each of the communication terminals

(7) The test server according to any one of (1) to (6), in which

-   -   the test information items acquired from the communication        terminals each include patient attribute information indicating        an attribute of a patient who is subjected to the test, and    -   the control unit performs, when receiving a demand to narrow        down statistical information from each of the communication        terminals, the statistical processing by performing        narrowing-down for test information items each having the        attribute of the patient attribute information, the demand        specifying any patient attribute information.

(8) The test server according to any one of (1) to (7), in which

-   -   the test information items acquired from the communication        terminals each include terminal attribute information indicating        an attribute of each of the communication terminals that        performs the test, and    -   the control unit performs, when receiving a demand to narrow        down statistical information from each of the communication        terminals, the statistical processing by performing        narrowing-down for test information items each having the        attribute of the terminal attribute information, the demand        specifying any terminal attribute information

(9) The test server according to (8), in which

-   -   the control unit performs weighting on the result of the        statistical processing, the weighting being based on the        terminal attribute information, the result of the statistical        processing being calculated based on the test information items        obtained by narrowing-down.

(10) The test server according to any one of (2) to (4), in which

-   -   the control unit is capable of using a positive rate instead of        the prevalence rate.

(11) The test server according to (10), in which

-   -   the test information item includes information for identifying a        method of performing the test, and    -   the control unit is capable of using the positive rate instead        of the prevalence rate in each of the methods of performing the        test for an identical disease, the positive rate being the        result of the statistical processing performed on a plurality of        test information items acquired by one of the methods, the        method satisfying preliminarily demanded predetermined values of        a sensitivity and a specificity, out of sensitivities and        specificities preliminarily provided to the respective methods,        the prevalence rate being the result of the statistical        processing performed on each of a plurality of test information        items acquired by another one of the methods.

(12) The test server according to any one of (2) to (4), in which

-   -   the control unit evaluates effectiveness of the test based on        the positive predictive value, transmits an evaluation result to        each of the communication terminals, and causes each of the        communication terminals to present a message of recommendation        or non-recommendation for the test.

(13) The test server according to any one of (2) to (4), in which

-   -   the test information items acquired from the communication        terminals each include information of a region in which each of        the communication terminals is located, as terminal attribute        information indicating an attribute of each of the communication        terminals that performs the test, and    -   the control unit estimates the prevalence rate in a first region        in which the test is not implemented, based on prevalence rates        obtained in one or more second regions that are different from        the first region, and based on a factor having an influence on        infection between each of the second regions and the first        region.

(14) The test server according to any one of (2) to (4), in which

-   -   the control unit periodically performs the statistical        processing and creates history information of the prevalence        rate, and predicts a future prevalence rate based on the history        information.

(15) The test server according to any one of (1) to (14), in which

-   -   the control unit returns a result of the statistical processing        acquired from outside, instead of performing the statistical        processing on the plurality of stored test information items.

(16) The test server according to any one of (1) to (15), in which

-   -   the control unit transmits a list of medicines to each of the        communication terminals, the medicines being based on at least        one of the result of the test, the diagnosis, and the result of        the statistical processing, and causes each of the communication        terminals to present the list as medicines recommended for        medication, or    -   the control unit causes each of the communication terminals to        present a list of methods for the test capable of being        performed in the test device, a recommendation mark indicating a        method for a test recommended in the list, and a user interface        for starting the test.

(17) A communication terminal, including:

-   -   a communication unit that communicates with a test server via a        network, the test server collecting a plurality of sets of at        least one of a result of a test on the presence or absence of a        disease and a diagnosis on the presence or absence of the        disease as test information items, and providing a result of        statistical processing performed on the plurality of collected        test information items, the diagnosis being related to the test        and made by a doctor;    -   an input unit that receives an input from a user or the doctor;        and    -   a control unit that        -   causes the communication unit to transmit a demand of the            result of the statistical processing to the test server,        -   causes a test device to execute the test,        -   presents the result of the statistical processing and a            result of the executed test to the user, the result of the            statistical processing being received via the communication            unit from the test server,        -   causes the user to input the diagnosis on the executed test,            using the input unit, and        -   causes the communication unit to transmit at least one of            the result of the executed test and the input diagnosis as            the test information item to the test server.

(18) A test system, including:

-   -   a test server; and    -   a plurality of communication terminals,    -   the test server including        -   a first communication unit that communicates with the            plurality of communication terminals via a network, and        -   a first control unit that            -   acquires at least one of a result of a test on the                presence or absence of a disease and a diagnosis on the                presence or absence of the disease as a test information                item from each of the plurality of communication                terminals via the communication unit, the diagnosis                being related to the test and made by a doctor,            -   causes a storage unit to store the plurality of acquired                test information items therein,            -   performs statistical processing on the plurality of                stored test information items, and            -   causes the communication unit to return a result of the                statistical processing according to a demand given from                each of the communication terminals before the doctor                makes a diagnosis,    -   the plurality of communication terminals each including        -   a second communication unit that communicates with the test            server via the network,        -   an input unit that receives an input from a user or the            doctor, and        -   a second control unit that            -   causes the communication unit to transmit the demand of                the result of the statistical processing to the test                server,            -   causes a test device to execute the test,            -   presents the result of the statistical processing and a                result of the executed test to the user, the result of                the statistical processing being received via the                communication unit from the test server,            -   causes the user to input the diagnosis on the executed                test, using the input unit, and            -   causes the communication unit to transmit at least one                of the result of the executed test and the input                diagnosis as the test information item to the test                server.

(19) A test method, including:

-   -   by a control unit,    -   acquiring, from a plurality of communication terminals each        being connectable to a test device capable of executing a test        on the presence or absence of a disease and each being capable        of inputting a diagnosis on the presence or absence of the        disease, at least one of a result of the test and the diagnosis        as a test information item via the communication unit, the        diagnosis being related to the test and made by a doctor;    -   causing a storage unit to store the plurality of acquired test        information items therein;    -   performing statistical processing on the plurality of stored        test information items; and    -   causing the communication unit to return a result of the        statistical processing according to a demand given from each of        the communication terminals before the doctor makes a diagnosis.

(20) A test method, including:

-   -   by a control unit,    -   causing a communication unit to transmit a demand of a result of        statistical processing to a test server, the communication unit        communicating with the test server via a network, the test        server collecting a plurality of sets of at least one of a        result of a test on the presence or absence of a disease and a        diagnosis on the presence or absence of the disease as test        information items, and providing the result of the statistical        processing performed on the plurality of collected test        information items, the diagnosis being related to the test and        made by a doctor;    -   causing the communication unit to transmit the demand of the        result of the statistical processing to the test server;    -   causing a test device to execute the test;    -   presenting the result of the statistical processing and a result        of the executed test to a user or the doctor, the result of the        statistical processing being received via the communication unit        from the test server;    -   causing the user to input the diagnosis on the executed test,        using an input unit that receives an input from the user; and    -   causing the communication unit to transmit at least one of the        result of the executed test and the input diagnosis as the test        information item to the test server.

DESCRIPTION OF SYMBOLS

-   10 test system-   20 test terminal-   21 CPU-   22 ROM-   23 RAM-   24 operation input unit-   25 network interface unit-   26 display unit-   27 storage unit-   28 test device-   30 network (Internet)-   40 test server-   41 CPU-   42 ROM-   43 RAM-   44 operation input unit-   45 network interface unit-   46 display unit-   47 storage unit-   47 a database

1. A prevalence rate prediction method for infectious disease,comprising: communicating with a plurality of communication terminalsvia a network, wherein each of the plurality of communication terminalsis connected to a test device, wherein the test device is configured toexecute a test on one of a presence or an absence of the infectiousdisease, and each of the plurality of communication terminals inputs adiagnosis on the one of the presence or the absence of the infectiousdisease; acquiring a plurality of test information from the plurality ofcommunication terminals, wherein each of the plurality of testinformation comprises at least one of a result of the test or a resultof the diagnosis; controlling a storage device to store the acquiredplurality of test information; performing statistical process on thestored plurality of test information, wherein at least one of a resultof the statistical process is a current prevalence rate; outputting theresult of the statistical process based on a demand given from each ofthe plurality of communication terminals before doctor's diagnosis; andpredicting a future prevalence rate based on the current prevalence rateand a prevalence rate that is before a specific period.
 2. Theprevalence rate prediction method according to claim 1, wherein theresult of the statistical process is at least one of a positivepredictive value or a negative predictive value, and the result of thestatistical process is outputted based on: a number of at least onefirst test information of the plurality of test information in which theresult of the test and the result of the diagnosis are positive; anumber of at least one second test information of the plurality of testinformation in which the result of the test is negative and the resultof the diagnosis is positive; a number of at least one third testinformation of the plurality of test information in which the result ofthe test is positive and the result of the diagnosis is negative; and anumber of at least one fourth test information of the plurality of testinformation in which the result of the test and the result of thediagnosis are negative.
 3. The prevalence rate prediction methodaccording to claim 1, further comprising calculating a change rate of afuture prevalence rate per unit time based on the current prevalencerate and the prevalence rate that is before the specific period.
 4. Theprevalence rate prediction method according to claim 1, furthercomprising providing a warning information to a user based on the futureprevalence rate, wherein the warning information corresponds toinformation of future infection spread.
 5. The prevalence rateprediction method according to claim 4, wherein the warning informationis provided based on the predicted future prevalence rate exceeds aspecific threshold value.
 6. The prevalence rate prediction methodaccording to claim 1, further comprising correcting the currentprevalence rate based on at least one of patient attribute information,terminal attribute information, or location information.
 7. Theprevalence rate prediction method according to claim 6, furthercomprising: performing weighting based on at least one of the patientattribute information, the terminal attribute information, or thelocation information; and correcting the current prevalence rate basedon the performed weighting.
 8. The prevalence rate prediction methodaccording to claim 6, further comprising: performing narrowing down oftarget data based on at least one of the patient attribute information,the terminal attribute information, or the location information; andcorrecting the current prevalence rate based on the performed narrowingdown of the target data.
 9. The prevalence rate prediction methodaccording to claim 6, wherein the patient attribute information includesat least one of medical interview information, medication information,previous disease, physical information, lifestyle habits information,genotype, microbial flora of patients, or race.
 10. The prevalence rateprediction method according to claim 6, wherein the terminal attributeinformation includes region information that is associated with a testterminal.
 11. The prevalence rate prediction method according to claim10, further comprising estimating the current prevalence rate based on adistance between the current position of the test terminal and theposition that is associated with execution of the test, wherein theterminal attribute information includes a current position of the testterminal and a position that is associated with execution of the test.12. The prevalence rate prediction method according to claim 10, furthercomprising acquiring an immunization penetration rate in anadministrative district, wherein a communication terminal of theplurality of communication terminals is associated with theadministrative district; and correcting the current prevalence ratebased on the acquired immunization penetration rate.
 13. The prevalencerate prediction method according to claim 6, wherein the locationinformation includes information of first region information in whichthe test is not implemented and information of at least one secondregion in which the test is implemented, and the at least one secondregion is different from the first region.
 14. The prevalence rateprediction method according to claim 13, further comprising estimatingthe current prevalence rate in the first region based on a plurality ofprevalence rates and a factor having an influence on infection betweeneach of the at least one second region and the first region, wherein theplurality of prevalence rates is associated with the at least one secondregion, and the plurality of prevalence rates is different from thecurrent prevalence rate, future prevalence rate, and the prevalence ratethat is before the specific period.
 15. The prevalence rate predictionmethod according to claim 1, wherein the result of the test isassociated with execution of various types of tests.
 16. The prevalencerate prediction method for infectious according to claim 1, furthercomprising calculating a positive rate of the stored plurality of testinformation instead of the current prevalence rate.
 17. The prevalencerate prediction method according to claim 1, further comprising:evaluating effectiveness of the test based on a positive predictivevalue; transmitting an evaluation result of effectiveness of the test toeach of the plurality of communication terminals; and causing each ofthe plurality of communication terminals to output a message of one ofrecommendation or non-recommendation for the test.
 18. The prevalencerate prediction method according to claim 1, acquiring the plurality ofstored test information from the test device.
 19. A test server topredict a prevalence rate of infectious disease, comprising: circuitryconfigured to: communicate with a plurality of communication terminalsvia a network, wherein each of the plurality of communication terminalsis connected to a test device, wherein the test device is configured toexecute a test on one of a presence or an absence of the infectiousdisease, and each of the plurality of communication terminals inputs adiagnosis on the one of the presence or the absence of the infectiousdisease; acquire a plurality of test information from the plurality ofcommunication terminals, wherein each of the plurality of testinformation comprises at least one of a result of the test or a resultof the diagnosis; control a storage device to store the acquiredplurality of test information; perform statistical process on the storedplurality of test information, wherein at least one of a result of thestatistical process is a current prevalence rate; output the result ofthe statistical process based on a demand given from each of theplurality of communication terminals before doctor's diagnosis; andpredict a future prevalence rate based on the current prevalence rateand a prevalence rate that is before a specific period.
 20. A testsystem to predict a prevalence rate of infectious disease, comprising atest device that executes a test on one of a presence or an absence of adisease; and a test server that comprises circuitry configured to:communicate with a plurality of communication terminals via a network,wherein each of the plurality of communication terminals is connected toa test device, wherein the test device is configured to execute a teston one of a presence or an absence of the infectious disease, and eachof the plurality of communication terminals inputs a diagnosis on theone of the presence or the absence of the infectious disease; acquire aplurality of test information from the plurality of communicationterminals, wherein each of the plurality of test information comprisesat least one of a result of the test or a result of the diagnosis;control a storage device to store the acquired plurality of testinformation; perform statistical process on the stored plurality of testinformation, wherein at least one of a result of the statistical processis a current prevalence rate; output the result of the statisticalprocess based on a demand given from each of the plurality ofcommunication terminals before doctor's diagnosis; and predict a futureprevalence rate based on the current prevalence rate and a prevalencerate that is before a specific period.